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models of Markov (HMM)</title> <meta name="description" content="Search results for: hidden models of Markov (HMM)"> <meta name="keywords" content="hidden models of Markov (HMM)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="hidden models of Markov (HMM)"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 7219</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: hidden models of Markov (HMM)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7219</span> A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pavan%20K.%20Rallabandi">Pavan K. Rallabandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kailash%20C.%20Patidar"> Kailash C. Patidar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20systems" title="hybrid systems">hybrid systems</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models" title=" hidden markov models"> hidden markov models</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20networks" title=" recurrent neural networks"> recurrent neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deterministic%20finite%20state%20automata" title=" deterministic finite state automata"> deterministic finite state automata</a> </p> <a href="https://publications.waset.org/abstracts/37759/a-hybrid-system-of-hidden-markov-models-and-recurrent-neural-networks-for-learning-deterministic-finite-state-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7218</span> Recognition of Cursive Arabic Handwritten Text Using Embedded Training Based on Hidden Markov Models (HMMs)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabi%20Mouhcine">Rabi Mouhcine</a>, <a href="https://publications.waset.org/abstracts/search?q=Amrouch%20Mustapha"> Amrouch Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahani%20Zouhir"> Mahani Zouhir</a>, <a href="https://publications.waset.org/abstracts/search?q=Mammass%20Driss"> Mammass Driss</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=recognition" title="recognition">recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=handwriting" title=" handwriting"> handwriting</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20text" title=" Arabic text"> Arabic text</a>, <a href="https://publications.waset.org/abstracts/search?q=HMMs" title=" HMMs"> HMMs</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20training" title=" embedded training"> embedded training</a> </p> <a href="https://publications.waset.org/abstracts/54405/recognition-of-cursive-arabic-handwritten-text-using-embedded-training-based-on-hidden-markov-models-hmms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54405.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7217</span> The Combination of the Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), JITTER and SHIMMER Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim-Fares%20Zaidi">Brahim-Fares Zaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Boudraa"> Malika Boudraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sid-Ahmed%20Selouani"> Sid-Ahmed Selouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model%20toolkit%20%28HTK%29" title="hidden Markov model toolkit (HTK)">hidden Markov model toolkit (HTK)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29" title=" hidden models of Markov (HMM)"> hidden models of Markov (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel-frequency%20cepstral%20coefficients%20%28MFCC%29" title=" Mel-frequency cepstral coefficients (MFCC)"> Mel-frequency cepstral coefficients (MFCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptual%20linear%20prediction%20%28PLP%E2%80%99s%29" title=" perceptual linear prediction (PLP’s)"> perceptual linear prediction (PLP’s)</a> </p> <a href="https://publications.waset.org/abstracts/143303/the-combination-of-the-mel-frequency-cepstral-coefficients-mfcc-perceptual-linear-prediction-plp-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143303.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7216</span> Hidden Markov Movement Modelling with Irregular Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Victoria%20Goodall">Victoria Goodall</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Fatti"> Paul Fatti</a>, <a href="https://publications.waset.org/abstracts/search?q=Norman%20Owen-Smith"> Norman Owen-Smith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20Models" title="hidden Markov Models">hidden Markov Models</a>, <a href="https://publications.waset.org/abstracts/search?q=irregular%20observations" title=" irregular observations"> irregular observations</a>, <a href="https://publications.waset.org/abstracts/search?q=animal%20movement%20modelling" title=" animal movement modelling"> animal movement modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=nocturnal%20predator" title=" nocturnal predator"> nocturnal predator</a> </p> <a href="https://publications.waset.org/abstracts/56744/hidden-markov-movement-modelling-with-irregular-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56744.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7215</span> Speed Breaker/Pothole Detection Using Hidden Markov Models: A Deep Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Surajit%20Chakrabarty">Surajit Chakrabarty</a>, <a href="https://publications.waset.org/abstracts/search?q=Piyush%20Chauhan"> Piyush Chauhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Subhasis%20Panda"> Subhasis Panda</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujoy%20Bhattacharya"> Sujoy Bhattacharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=pothole" title=" pothole"> pothole</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20breaker" title=" speed breaker"> speed breaker</a> </p> <a href="https://publications.waset.org/abstracts/121459/speed-breakerpothole-detection-using-hidden-markov-models-a-deep-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7214</span> Recognition of Voice Commands of Mentor Robot in Noisy Environment Using Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khenfer%20Koummich%20Fatma">Khenfer Koummich Fatma</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendel%20Fatiha"> Hendel Fatiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesbahi%20Larbi"> Mesbahi Larbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a human-machine interface with a voice recognition system that allows the operator to teleoperate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands pronounced in two languages: French and Arabic. The obtained recognition rate is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equals 30 dB, in this case; the Arabic speech recognition rate is 69%, and the French speech recognition rate is 80%. This can be explained by the ability of phonetic context of each speech when the noise is added. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20speech%20recognition" title="Arabic speech recognition">Arabic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model%20%28HMM%29" title=" Hidden Markov Model (HMM)"> Hidden Markov Model (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK" title=" HTK"> HTK</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=TIMIT" title=" TIMIT"> TIMIT</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20command" title=" voice command"> voice command</a> </p> <a href="https://publications.waset.org/abstracts/67988/recognition-of-voice-commands-of-mentor-robot-in-noisy-environment-using-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7213</span> Artificial Neural Networks and Hidden Markov Model in Landslides Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Subhashini">C. S. Subhashini</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20L.%20Premaratne"> H. L. Premaratne </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslides" title="landslides">landslides</a>, <a href="https://publications.waset.org/abstracts/search?q=influencing%20factors" title=" influencing factors"> influencing factors</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20model" title=" neural network model"> neural network model</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title=" hidden markov model"> hidden markov model</a> </p> <a href="https://publications.waset.org/abstracts/21014/artificial-neural-networks-and-hidden-markov-model-in-landslides-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7212</span> Simulating the Hot Hand Phenomenon in Basketball with Bayesian Hidden Markov Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20%20Calvo">Gabriel Calvo</a>, <a href="https://publications.waset.org/abstracts/search?q=Carmen%20Armero"> Carmen Armero</a>, <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Spezia"> Luigi Spezia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A basketball player is said to have a hot hand if his/her performance is better than expected in different periods of time. A way to deal with this phenomenon is to make use of latent variables, which can indicate whether the player is ‘on fire’ or not. This work aims to model the hot hand phenomenon through a Bayesian hidden Markov model (HMM) with two states (cold and hot) and two different probability of success depending on the corresponding hidden state. This task is illustrated through a comprehensive simulation study. The simulated data sets emulate the field goal attempts in an NBA season from different profile players. This model can be a powerful tool to assess the ‘streakiness’ of each player, and it provides information about the general performance of the players during the match. Finally, the Bayesian HMM allows computing the posterior probability of any type of streak. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernoulli%20trials" title="Bernoulli trials">Bernoulli trials</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20goals" title=" field goals"> field goals</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20variables" title=" latent variables"> latent variables</a>, <a href="https://publications.waset.org/abstracts/search?q=posterior%20distribution" title=" posterior distribution"> posterior distribution</a> </p> <a href="https://publications.waset.org/abstracts/135522/simulating-the-hot-hand-phenomenon-in-basketball-with-bayesian-hidden-markov-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135522.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">192</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7211</span> Excitation Modeling for Hidden Markov Model-Based Speech Synthesis Based on Wavelet Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kiran%20Reddy">M. Kiran Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sreenivasa%20Rao"> K. Sreenivasa Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The conventional Hidden Markov Model (HMM)-based speech synthesis system (HTS) uses only a pulse excitation model, which significantly differs from natural excitation signal. Hence, buzziness can be perceived in the speech generated using HTS. This paper proposes an efficient excitation modeling method that can significantly reduce the buzziness, and improve the quality of HMM-based speech synthesis. The proposed approach models the pitch-synchronous residual frames extracted from the residual excitation signal. Each pitch synchronous residual frame is parameterized using 30 wavelet coefficients. These 30 wavelet coefficients are found to accurately capture the perceptually important information present in the residual waveform. In synthesis phase, the residual frames are reconstructed from the generated wavelet coefficients and are pitch-synchronously overlap-added to generate the excitation signal. The proposed excitation modeling method is integrated into HMM-based speech synthesis system. Evaluation results indicate that the speech synthesized by the proposed excitation model is significantly better than the speech generated using state-of-the-art excitation modeling methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=excitation%20modeling" title="excitation modeling">excitation modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20models" title=" hidden Markov models"> hidden Markov models</a>, <a href="https://publications.waset.org/abstracts/search?q=pitch-synchronous%20frames" title=" pitch-synchronous frames"> pitch-synchronous frames</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20synthesis" title=" speech synthesis"> speech synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20coefficients" title=" wavelet coefficients"> wavelet coefficients</a> </p> <a href="https://publications.waset.org/abstracts/102457/excitation-modeling-for-hidden-markov-model-based-speech-synthesis-based-on-wavelet-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102457.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">248</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7210</span> Hidden Markov Model for the Simulation Study of Neural States and Intentionality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20B.%20Mishra">R. B. Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hidden Markov Model (HMM) has been used in prediction and determination of states that generate different neural activations as well as mental working conditions. This paper addresses two applications of HMM; one to determine the optimal sequence of states for two neural states: Active (AC) and Inactive (IA) for the three emission (observations) which are for No Working (NW), Waiting (WT) and Working (W) conditions of human beings. Another is for the determination of optimal sequence of intentionality i.e. Believe (B), Desire (D), and Intention (I) as the states and three observational sequences: NW, WT and W. The computational results are encouraging and useful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hiden%20markov%20model" title="hiden markov model">hiden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=believe%20desire%20intention" title=" believe desire intention"> believe desire intention</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20activation" title=" neural activation"> neural activation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/31030/hidden-markov-model-for-the-simulation-study-of-neural-states-and-intentionality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7209</span> Metamorphic Computer Virus Classification Using Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babak%20Bashari%20Rad">Babak Bashari Rad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20classification" title="malware classification">malware classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20virus%20classification" title=" computer virus classification"> computer virus classification</a>, <a href="https://publications.waset.org/abstracts/search?q=metamorphic%20virus" title=" metamorphic virus"> metamorphic virus</a>, <a href="https://publications.waset.org/abstracts/search?q=metamorphic%20malware" title=" metamorphic malware"> metamorphic malware</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a> </p> <a href="https://publications.waset.org/abstracts/62795/metamorphic-computer-virus-classification-using-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">315</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7208</span> Advances in Artificial intelligence Using Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20phonetic" title=" acoustic phonetic"> acoustic phonetic</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models%20%28HMM%29" title=" hidden markov models (HMM)"> hidden markov models (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20models%20of%20speech%20recognition" title=" statistical models of speech recognition"> statistical models of speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20machine%20performance" title=" human machine performance"> human machine performance</a> </p> <a href="https://publications.waset.org/abstracts/26319/advances-in-artificial-intelligence-using-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7207</span> The Combination of the Mel Frequency Cepstral Coefficients, Perceptual Linear Prediction, Jitter and Shimmer Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Fares%20Zaidi">Brahim Fares Zaidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARSDS" title="ARSDS">ARSDS</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK" title=" HTK"> HTK</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM" title=" HMM"> HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=PLP" title=" PLP"> PLP</a> </p> <a href="https://publications.waset.org/abstracts/158636/the-combination-of-the-mel-frequency-cepstral-coefficients-perceptual-linear-prediction-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">108</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7206</span> Exploring the Activity Fabric of an Intelligent Environment with Hierarchical Hidden Markov Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chiung-Hui%20Chen">Chiung-Hui Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=behavior" title="behavior">behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20hidden%20Markov%20model" title=" hierarchical hidden Markov model"> hierarchical hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20object" title=" intelligent object"> intelligent object</a> </p> <a href="https://publications.waset.org/abstracts/68970/exploring-the-activity-fabric-of-an-intelligent-environment-with-hierarchical-hidden-markov-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">233</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7205</span> Hidden Markov Model for Financial Limit Order Book and Its Application to Algorithmic Trading Strategy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sriram%20Kashyap%20Prasad">Sriram Kashyap Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ionut%20Florescu"> Ionut Florescu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study models the intraday asset prices as driven by Markov process. This work identifies the latent states of the Hidden Markov model, using limit order book data (trades and quotes) to continuously estimate the states throughout the day. This work builds a trading strategy using estimated states to generate signals. The strategy utilizes current state to recalibrate buy/ sell levels and the transition between states to trigger stop-loss when adverse price movements occur. The proposed trading strategy is tested on the Stevens High Frequency Trading (SHIFT) platform. SHIFT is a highly realistic market simulator with functionalities for creating an artificial market simulation by deploying agents, trading strategies, distributing initial wealth, etc. In the implementation several assets on the NASDAQ exchange are used for testing. In comparison to a strategy with static buy/ sell levels, this study shows that the number of limit orders that get matched and executed can be increased. Executing limit orders earns rebates on NASDAQ. The system can capture jumps in the limit order book prices, provide dynamic buy/sell levels and trigger stop loss signals to improve the PnL (Profit and Loss) performance of the strategy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithmic%20trading" title="algorithmic trading">algorithmic trading</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20model" title=" Hidden Markov model"> Hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20trading" title=" high frequency trading"> high frequency trading</a>, <a href="https://publications.waset.org/abstracts/search?q=limit%20order%20book%20learning" title=" limit order book learning"> limit order book learning</a> </p> <a href="https://publications.waset.org/abstracts/134323/hidden-markov-model-for-financial-limit-order-book-and-its-application-to-algorithmic-trading-strategy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134323.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7204</span> Bayesian Hidden Markov Modelling of Blood Type Distribution for COVID-19 Cases Using Poisson Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Johnson%20Joseph%20Kwabina%20Arhinful">Johnson Joseph Kwabina Arhinful</a>, <a href="https://publications.waset.org/abstracts/search?q=Owusu-Ansah%20Emmanuel%20Degraft%20Johnson"> Owusu-Ansah Emmanuel Degraft Johnson</a>, <a href="https://publications.waset.org/abstracts/search?q=Okyere%20Gabrial%20Asare"> Okyere Gabrial Asare</a>, <a href="https://publications.waset.org/abstracts/search?q=Adebanji%20Atinuke%20Olusola"> Adebanji Atinuke Olusola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a model to describe the blood types distribution of new Coronavirus (COVID-19) cases using the Bayesian Poisson - Hidden Markov Model (BP-HMM). With the help of the Gibbs sampler algorithm, using OpenBugs, the study first identifies the number of hidden states fitting European (EU) and African (AF) data sets of COVID-19 cases by blood type frequency. The study then compares the state-dependent mean of infection within and across the two geographical areas. The study findings show that the number of hidden states and infection rates within and across the two geographical areas differ according to blood type. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BP-HMM" title="BP-HMM">BP-HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=blood%20types" title=" blood types"> blood types</a>, <a href="https://publications.waset.org/abstracts/search?q=GIBBS%20sampler" title=" GIBBS sampler"> GIBBS sampler</a> </p> <a href="https://publications.waset.org/abstracts/156185/bayesian-hidden-markov-modelling-of-blood-type-distribution-for-covid-19-cases-using-poisson-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156185.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7203</span> An Optimal Bayesian Maintenance Policy for a Partially Observable System Subject to Two Failure Modes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akram%20Khaleghei%20Ghosheh%20Balagh">Akram Khaleghei Ghosheh Balagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new maintenance model for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model. A cost-optimal Bayesian control policy is developed for maintaining the system. The control problem is formulated in the semi-Markov decision process framework. An effective computational algorithm is developed and illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title="partially observable system">partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20Bayesian%20control" title=" multivariate Bayesian control"> multivariate Bayesian control</a> </p> <a href="https://publications.waset.org/abstracts/12740/an-optimal-bayesian-maintenance-policy-for-a-partially-observable-system-subject-to-two-failure-modes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7202</span> An Automatic Speech Recognition Tool for the Filipino Language Using the HTK System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Lorenzo%20Bautista">John Lorenzo Bautista</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoon-Joong%20Kim"> Yoon-Joong Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the development of a Filipino speech recognition tool using the HTK System. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic HMM-based (Hidden-Markov Model) acoustic models. Experiments on different mixture-weights were incorporated in the study. The phoneme-level word-based recognition of a 5-state HMM resulted in an average accuracy rate of 80.13 for a single-Gaussian mixture model, 81.13 after implementing a phoneme-alignment, and 87.19 for the increased Gaussian-mixture weight model. The highest accuracy rate of 88.70% was obtained from a 5-state model with 6 Gaussian mixtures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filipino%20language" title="Filipino language">Filipino language</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK%20system" title=" HTK system"> HTK system</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a> </p> <a href="https://publications.waset.org/abstracts/10240/an-automatic-speech-recognition-tool-for-the-filipino-language-using-the-htk-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10240.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7201</span> Markov Switching of Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josip%20Arneric">Josip Arneric</a>, <a href="https://publications.waset.org/abstracts/search?q=Blanka%20Skrabic%20Peric"> Blanka Skrabic Peric</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emerging%20markets" title="emerging markets">emerging markets</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20switching" title=" Markov switching"> Markov switching</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20model" title=" GARCH model"> GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probabilities" title=" transition probabilities"> transition probabilities</a> </p> <a href="https://publications.waset.org/abstracts/23987/markov-switching-of-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23987.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7200</span> Analysis of Linguistic Disfluencies in Bilingual Children’s Discourse</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheena%20Christabel%20Pravin">Sheena Christabel Pravin</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Palanivelan"> M. Palanivelan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bi-lingual" title="bi-lingual">bi-lingual</a>, <a href="https://publications.waset.org/abstracts/search?q=children%20who%20stutter" title=" children who stutter"> children who stutter</a>, <a href="https://publications.waset.org/abstracts/search?q=children%20with%20language%20impairment" title=" children with language impairment"> children with language impairment</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models" title=" hidden markov models"> hidden markov models</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20disfluencies" title=" linguistic disfluencies"> linguistic disfluencies</a>, <a href="https://publications.waset.org/abstracts/search?q=stuttering%20disfluencies" title=" stuttering disfluencies"> stuttering disfluencies</a> </p> <a href="https://publications.waset.org/abstracts/87075/analysis-of-linguistic-disfluencies-in-bilingual-childrens-discourse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">217</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7199</span> A Review of Different Studies on Hidden Markov Models for Multi-Temporal Satellite Images: Stationarity and Non-Stationarity Issues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Ben%20Abbes">Ali Ben Abbes</a>, <a href="https://publications.waset.org/abstracts/search?q=Imed%20Riadh%20Farah"> Imed Riadh Farah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the considerable advances in Multi-Temporal Satellite Images (MTSI), remote sensing application became more accurate. Recently, many advances in modeling MTSI are developed using various models. The purpose of this article is to present an overview of studies using Hidden Markov Model (HMM). First of all, we provide a background of using HMM and their applications in this context. A comparison of the different works is discussed, and possible areas and challenges are highlighted. Secondly, we discussed the difference on vegetation monitoring as well as urban growth. Nevertheless, most research efforts have been used only stationary data. From another point of view, in this paper, we describe a new non-stationarity HMM, that is defined with a set of parts of the time series e.g. seasonal, trend and random. In addition, a new approach giving more accurate results and improve the applicability of the HMM in modeling a non-stationary data series. In order to assess the performance of the HMM, different experiments are carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the northwestern region of Tunisia and Landsat time series of tres Cantos-Madrid in Spain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-temporal%20satellite%20image" title="multi-temporal satellite image">multi-temporal satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM" title=" HMM "> HMM </a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationarity" title=" nonstationarity"> nonstationarity</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=urban" title=" urban"> urban</a> </p> <a href="https://publications.waset.org/abstracts/81836/a-review-of-different-studies-on-hidden-markov-models-for-multi-temporal-satellite-images-stationarity-and-non-stationarity-issues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81836.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7198</span> Markov-Chain-Based Optimal Filtering and Smoothing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garry%20A.%20Einicke">Garry A. Einicke</a>, <a href="https://publications.waset.org/abstracts/search?q=Langford%20B.%20White"> Langford B. White</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an optimum filter and smoother for recovering a Markov process message from noisy measurements. The developments follow from an equivalence between a state space model and a hidden Markov chain. The ensuing filter and smoother employ transition probability matrices and approximate probability distribution vectors. The properties of the optimum solutions are retained, namely, the estimates are unbiased and minimize the variance of the output estimation error, provided that the assumed parameter set are correct. Methods for estimating unknown parameters from noisy measurements are discussed. Signal recovery examples are described in which performance benefits are demonstrated at an increased calculation cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20filtering" title="optimal filtering">optimal filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=smoothing" title=" smoothing"> smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chains" title=" Markov chains"> Markov chains</a> </p> <a href="https://publications.waset.org/abstracts/20256/markov-chain-based-optimal-filtering-and-smoothing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20256.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7197</span> A Fast, Reliable Technique for Face Recognition Based on Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sameh%20Abaza">Sameh Abaza</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ibrahim"> Mohamed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Mahmoud"> Tarek Mahmoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the development in the digital image processing, its wide use in many applications such as medical, security, and others, the need for more accurate techniques that are reliable, fast and robust is vehemently demanded. In the field of security, in particular, speed is of the essence. In this paper, a pattern recognition technique that is based on the use of Hidden Markov Model (HMM), K-means and the Sobel operator method is developed. The proposed technique is proved to be fast with respect to some other techniques that are investigated for comparison. Moreover, it shows its capability of recognizing the normal face (center part) as well as face boundary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HMM" title="HMM">HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=K-Means" title=" K-Means"> K-Means</a>, <a href="https://publications.waset.org/abstracts/search?q=Sobel" title=" Sobel"> Sobel</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a> </p> <a href="https://publications.waset.org/abstracts/60973/a-fast-reliable-technique-for-face-recognition-based-on-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60973.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7196</span> Modeling Usage Patterns of Mobile App Service in App Market Using Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yangrae%20Cho">Yangrae Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinseok%20Kim"> Jinseok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongtae%20Park"> Yongtae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile app service ecosystem has been abruptly emerged, explosively grown, and dynamically transformed. In contrast with product markets in which product sales directly cause increment in firm’s income, customer’s usage is less visible but more valuable in service market. Especially, the market situation with cutthroat competition in mobile app store makes securing and keeping of users as vital. Although a few service firms try to manage their apps’ usage patterns by fitting on S-curve or applying other forecasting techniques, the time series approaches based on past sequential data are subject to fundamental limitation in the market where customer’s attention is being moved unpredictably and dynamically. We therefore propose a new conceptual approach for detecting usage pattern of mobile app service with Hidden Markov Model (HMM) which is based on the dual stochastic structure and mainly used to clarify unpredictable and dynamic sequential patterns in voice recognition or stock forecasting. Our approach could be practically utilized for app service firms to manage their services’ lifecycles and academically expanded to other markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20app%20service" title="mobile app service">mobile app service</a>, <a href="https://publications.waset.org/abstracts/search?q=usage%20pattern" title=" usage pattern"> usage pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20detection" title=" pattern detection"> pattern detection</a> </p> <a href="https://publications.waset.org/abstracts/40873/modeling-usage-patterns-of-mobile-app-service-in-app-market-using-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40873.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7195</span> Voice Commands Recognition of Mentor Robot in Noisy Environment Using HTK</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khenfer-Koummich%20Fatma">Khenfer-Koummich Fatma</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendel%20Fatiha"> Hendel Fatiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesbahi%20Larbi"> Mesbahi Larbi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> this paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a man-machine interface with a voice recognition system that allows the operator to tele-operate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands spoken in two languages: French and Arabic. The recognition rate obtained is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equal to 30 db, the Arabic speech recognition rate is 69% and 80% for French speech recognition rate. This can be explained by the ability of phonetic context of each speech when the noise is added. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=voice%20command" title="voice command">voice command</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM" title=" HMM"> HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=TIMIT" title=" TIMIT"> TIMIT</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK" title=" HTK"> HTK</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a> </p> <a href="https://publications.waset.org/abstracts/24454/voice-commands-recognition-of-mentor-robot-in-noisy-environment-using-htk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7194</span> Part of Speech Tagging Using Statistical Approach for Nepali Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Archit%20Yajnik">Archit Yajnik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title="hidden markov model">hidden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=POS%20tagging" title=" POS tagging"> POS tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=viterbi%20algorithm" title=" viterbi algorithm"> viterbi algorithm</a> </p> <a href="https://publications.waset.org/abstracts/61160/part-of-speech-tagging-using-statistical-approach-for-nepali-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61160.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7193</span> Volatility Model with Markov Regime Switching to Forecast Baht/USD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nop%20Sopipan">Nop Sopipan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Regime%20Switching" title=" Markov Regime Switching"> Markov Regime Switching</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Baht%2FUSD" title=" Baht/USD"> Baht/USD</a> </p> <a href="https://publications.waset.org/abstracts/3942/volatility-model-with-markov-regime-switching-to-forecast-bahtusd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7192</span> Residual Life Prediction for a System Subject to Condition Monitoring and Two Failure Modes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akram%20Khaleghei">Akram Khaleghei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghosheh%20Balagh"> Ghosheh Balagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate the residual life prediction problem for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model with unknown parameters. The parameter estimation procedure based on an EM algorithm is developed and the formulas for the conditional reliability function and the mean residual life are derived, illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title="partially observable system">partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20life%20prediction" title=" residual life prediction"> residual life prediction</a> </p> <a href="https://publications.waset.org/abstracts/6352/residual-life-prediction-for-a-system-subject-to-condition-monitoring-and-two-failure-modes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6352.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">415</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7191</span> Estimating Knowledge Flow Patterns of Business Method Patents with a Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoonjung%20An">Yoonjung An</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongtae%20Park"> Yongtae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge flows are a critical source of faster technological progress and stouter economic growth. Knowledge flows have been accelerated dramatically with the establishment of a patent system in which each patent is required by law to disclose sufficient technical information for the invention to be recreated. Patent analysis, thus, has been widely used to help investigate technological knowledge flows. However, the existing research is limited in terms of both subject and approach. Particularly, in most of the previous studies, business method (BM) patents were not covered although they are important drivers of knowledge flows as other patents. In addition, these studies usually focus on the static analysis of knowledge flows. Some use approaches that incorporate the time dimension, yet they still fail to trace a true dynamic process of knowledge flows. Therefore, we investigate dynamic patterns of knowledge flows driven by BM patents using a Hidden Markov Model (HMM). An HMM is a popular statistical tool for modeling a wide range of time series data, with no general theoretical limit in regard to statistical pattern classification. Accordingly, it enables characterizing knowledge patterns that may differ by patent, sector, country and so on. We run the model in sets of backward citations and forward citations to compare the patterns of knowledge utilization and knowledge dissemination. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20method%20patents" title="business method patents">business method patents</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20pattern" title=" dynamic pattern"> dynamic pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden-Markov%20Model" title=" Hidden-Markov Model"> Hidden-Markov Model</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20flow" title=" knowledge flow"> knowledge flow</a> </p> <a href="https://publications.waset.org/abstracts/40872/estimating-knowledge-flow-patterns-of-business-method-patents-with-a-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">328</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7190</span> Copula Markov Switching Multifractal Models for Forecasting Value-at-Risk </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giriraj%20Achari">Giriraj Achari</a>, <a href="https://publications.waset.org/abstracts/search?q=Malay%20Bhattacharyya"> Malay Bhattacharyya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the effectiveness of Copula Markov Switching Multifractal (MSM) models at forecasting Value-at-Risk of a two-stock portfolio is studied. The innovations are allowed to be drawn from distributions that can capture skewness and leptokurtosis, which are well documented empirical characteristics observed in financial returns. The candidate distributions considered for this purpose are Johnson-SU, Pearson Type-IV and α-Stable distributions. The two univariate marginal distributions are combined using the Student-t copula. The estimation of all parameters is performed by Maximum Likelihood Estimation. Finally, the models are compared in terms of accurate Value-at-Risk (VaR) forecasts using tests of unconditional coverage and independence. It is found that Copula-MSM-models with leptokurtic innovation distributions perform slightly better than Copula-MSM model with Normal innovations. Copula-MSM models, in general, produce better VaR forecasts as compared to traditional methods like Historical Simulation method, Variance-Covariance approach and Copula-Generalized Autoregressive Conditional Heteroscedasticity (Copula-GARCH) models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Copula" title="Copula">Copula</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Switching" title=" Markov Switching"> Markov Switching</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal" title=" multifractal"> multifractal</a>, <a href="https://publications.waset.org/abstracts/search?q=value-at-risk" title=" value-at-risk"> value-at-risk</a> </p> <a href="https://publications.waset.org/abstracts/115727/copula-markov-switching-multifractal-models-for-forecasting-value-at-risk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115727.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29&page=5">5</a></li> 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