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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.19078">arXiv:2409.19078</a> <span> [<a href="https://arxiv.org/pdf/2409.19078">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Differential privacy for protecting patient data in speech disorder detection using deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arasteh%2C+S+T">Soroosh Tayebi Arasteh</a>, <a href="/search/cs?searchtype=author&query=Lotfinia%2C+M">Mahshad Lotfinia</a>, <a href="/search/cs?searchtype=author&query=Perez-Toro%2C+P+A">Paula Andrea Perez-Toro</a>, <a href="/search/cs?searchtype=author&query=Arias-Vergara%2C+T">Tomas Arias-Vergara</a>, <a href="/search/cs?searchtype=author&query=Orozco-Arroyave%2C+J+R">Juan Rafael Orozco-Arroyave</a>, <a href="/search/cs?searchtype=author&query=Schuster%2C+M">Maria Schuster</a>, <a href="/search/cs?searchtype=author&query=Maier%2C+A">Andreas Maier</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+S+H">Seung Hee Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.19078v1-abstract-short" style="display: inline;"> Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensitive data raises critical privacy concerns. Although differential privacy (DP) has been explored in the medical imaging domain, its application in pathological speech analysis remains largely unexplored despite the equally… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19078v1-abstract-full').style.display = 'inline'; document.getElementById('2409.19078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19078v1-abstract-full" style="display: none;"> Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensitive data raises critical privacy concerns. Although differential privacy (DP) has been explored in the medical imaging domain, its application in pathological speech analysis remains largely unexplored despite the equally critical privacy concerns. This study is the first to investigate DP's impact on pathological speech data, focusing on the trade-offs between privacy, diagnostic accuracy, and fairness. Using a large, real-world dataset of 200 hours of recordings from 2,839 German-speaking participants, we observed a maximum accuracy reduction of 3.85% when training with DP with a privacy budget, denoted by 蔚, of 7.51. To generalize our findings, we validated our approach on a smaller dataset of Spanish-speaking Parkinson's disease patients, demonstrating that careful pretraining on large-scale task-specific datasets can maintain or even improve model accuracy under DP constraints. We also conducted a comprehensive fairness analysis, revealing that reasonable privacy levels (2<蔚<10) do not introduce significant gender bias, though age-related disparities may require further attention. Our results suggest that DP can effectively balance privacy and utility in speech disorder detection, but also highlight the unique challenges in the speech domain, particularly regarding the privacy-fairness trade-off. This provides a foundation for future work to refine DP methodologies and address fairness across diverse patient groups in real-world deployments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19078v1-abstract-full').style.display = 'none'; document.getElementById('2409.19078v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.16115">arXiv:2405.16115</a> <span> [<a href="https://arxiv.org/pdf/2405.16115">pdf</a>, <a href="https://arxiv.org/format/2405.16115">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT clinical terminology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kulyabin%2C+M">Mikhail Kulyabin</a>, <a href="/search/cs?searchtype=author&query=Sokolov%2C+G">Gleb Sokolov</a>, <a href="/search/cs?searchtype=author&query=Galaida%2C+A">Aleksandr Galaida</a>, <a href="/search/cs?searchtype=author&query=Maier%2C+A">Andreas Maier</a>, <a href="/search/cs?searchtype=author&query=Arias-Vergara%2C+T">Tomas Arias-Vergara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.16115v1-abstract-short" style="display: inline;"> The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally automated due to the complexity of medical ontologies and restricted access to medical texts for training Natural Language Processing models. In this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16115v1-abstract-full').style.display = 'inline'; document.getElementById('2405.16115v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.16115v1-abstract-full" style="display: none;"> The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally automated due to the complexity of medical ontologies and restricted access to medical texts for training Natural Language Processing models. In this paper, we proposed a method, "SNOBERT," of linking text spans in clinical notes to specific concepts in the SNOMED CT using BERT-based models. The method consists of two stages: candidate selection and candidate matching. The models were trained on one of the largest publicly available dataset of labeled clinical notes. SNOBERT outperforms other classical methods based on deep learning, as confirmed by the results of a challenge in which it was applied. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16115v1-abstract-full').style.display = 'none'; document.getElementById('2405.16115v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.08064">arXiv:2404.08064</a> <span> [<a href="https://arxiv.org/pdf/2404.08064">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s43856-024-00609-5">10.1038/s43856-024-00609-5 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Impact of Speech Anonymization on Pathology and Its Limits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arasteh%2C+S+T">Soroosh Tayebi Arasteh</a>, <a href="/search/cs?searchtype=author&query=Arias-Vergara%2C+T">Tomas Arias-Vergara</a>, <a href="/search/cs?searchtype=author&query=Perez-Toro%2C+P+A">Paula Andrea Perez-Toro</a>, <a href="/search/cs?searchtype=author&query=Weise%2C+T">Tobias Weise</a>, <a href="/search/cs?searchtype=author&query=Packhaeuser%2C+K">Kai Packhaeuser</a>, <a href="/search/cs?searchtype=author&query=Schuster%2C+M">Maria Schuster</a>, <a href="/search/cs?searchtype=author&query=Noeth%2C+E">Elmar Noeth</a>, <a href="/search/cs?searchtype=author&query=Maier%2C+A">Andreas Maier</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+S+H">Seung Hee Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.08064v4-abstract-short" style="display: inline;"> Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where priva… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08064v4-abstract-full').style.display = 'inline'; document.getElementById('2404.08064v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.08064v4-abstract-full" style="display: none;"> Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where privacy is especially vital, has not been extensively examined. This study investigates anonymization's impact on pathological speech across over 2,700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness. We explore both deep-learning-based and signal processing-based anonymization methods. We document substantial privacy improvements across disorders-evidenced by equal error rate increases up to 1933%, with minimal overall impact on utility. Specific disorders such as Dysarthria, Dysphonia, and Cleft Lip and Palate experience minimal utility changes, while Dysglossia shows slight improvements. Our findings underscore that the impact of anonymization varies substantially across different disorders. This necessitates disorder-specific anonymization strategies to optimally balance privacy with diagnostic utility. Additionally, our fairness analysis reveals consistent anonymization effects across most of the demographics. This study demonstrates the effectiveness of anonymization in pathological speech for enhancing privacy, while also highlighting the importance of customized and disorder-specific approaches to account for inversion attacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08064v4-abstract-full').style.display = 'none'; document.getElementById('2404.08064v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published in Communications Medicine</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Commun Med 4, (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.05912">arXiv:2201.05912</a> <span> [<a href="https://arxiv.org/pdf/2201.05912">pdf</a>, <a href="https://arxiv.org/format/2201.05912">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Common Phone: A Multilingual Dataset for Robust Acoustic Modelling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Klumpp%2C+P">Philipp Klumpp</a>, <a href="/search/cs?searchtype=author&query=Arias-Vergara%2C+T">Tom谩s Arias-Vergara</a>, <a href="/search/cs?searchtype=author&query=P%C3%A9rez-Toro%2C+P+A">Paula Andrea P茅rez-Toro</a>, <a href="/search/cs?searchtype=author&query=N%C3%B6th%2C+E">Elmar N枚th</a>, <a href="/search/cs?searchtype=author&query=Orozco-Arroyave%2C+J+R">Juan Rafael Orozco-Arroyave</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.05912v2-abstract-short" style="display: inline;"> Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function. An ideal dataset is not necessarily large in size, but large with respect to the amount of unique speakers, utilized hardware and varying recording conditions. This enables a machi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.05912v2-abstract-full').style.display = 'inline'; document.getElementById('2201.05912v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.05912v2-abstract-full" style="display: none;"> Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function. An ideal dataset is not necessarily large in size, but large with respect to the amount of unique speakers, utilized hardware and varying recording conditions. This enables a machine learning model to explore as much of the domain-specific input space as possible during parameter estimation. This work introduces Common Phone, a gender-balanced, multilingual corpus recorded from more than 11.000 contributors via Mozilla's Common Voice project. It comprises around 116 hours of speech enriched with automatically generated phonetic segmentation. A Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. The architecture achieved a PER of 18.1 % on the entire test set, computed with all 101 unique phonetic symbols, showing slight differences between the individual languages. We conclude that Common Phone provides sufficient variability and reliable phonetic annotation to help bridging the gap between research and application of acoustic models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.05912v2-abstract-full').style.display = 'none'; document.getElementById('2201.05912v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Pre-print submitted to LREC 2022 Link to Common Phone: https://zenodo.org/record/5846137</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.11514">arXiv:2112.11514</a> <span> [<a href="https://arxiv.org/pdf/2112.11514">pdf</a>, <a href="https://arxiv.org/ps/2112.11514">ps</a>, <a href="https://arxiv.org/format/2112.11514">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.csl.2021.101321">10.1016/j.csl.2021.101321 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Phonetic Footprint of Parkinson's Disease </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Klumpp%2C+P">Philipp Klumpp</a>, <a href="/search/cs?searchtype=author&query=Arias-Vergara%2C+T">Tom谩s Arias-Vergara</a>, <a href="/search/cs?searchtype=author&query=V%C3%A1squez-Correa%2C+J+C">Juan Camilo V谩squez-Correa</a>, <a href="/search/cs?searchtype=author&query=P%C3%A9rez-Toro%2C+P+A">Paula Andrea P茅rez-Toro</a>, <a href="/search/cs?searchtype=author&query=Orozco-Arroyave%2C+J+R">Juan Rafael Orozco-Arroyave</a>, <a href="/search/cs?searchtype=author&query=Batliner%2C+A">Anton Batliner</a>, <a href="/search/cs?searchtype=author&query=N%C3%B6th%2C+E">Elmar N枚th</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.11514v1-abstract-short" style="display: inline;"> As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients. The complex interplay of different articulators during speech production and realization of required muscle tension become increasingly difficult, thus leading to a dysarthric speech. Characteristic patterns such as vowel instability, slurred pronunciati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.11514v1-abstract-full').style.display = 'inline'; document.getElementById('2112.11514v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.11514v1-abstract-full" style="display: none;"> As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients. The complex interplay of different articulators during speech production and realization of required muscle tension become increasingly difficult, thus leading to a dysarthric speech. Characteristic patterns such as vowel instability, slurred pronunciation and slow speech can often be observed in the affected individuals and were analyzed in previous studies to determine the presence and progression of PD. In this work, we used a phonetic recognizer trained exclusively on healthy speech data to investigate how PD affected the phonetic footprint of patients. We rediscovered numerous patterns that had been described in previous contributions although our system had never seen any pathological speech previously. Furthermore, we could show that intermediate activations from the neural network could serve as feature vectors encoding information related to the disease state of individuals. We were also able to directly correlate the expert-rated intelligibility of a speaker with the mean confidence of phonetic predictions. Our results support the assumption that pathological data is not necessarily required to train systems that are capable of analyzing PD speech. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.11514v1-abstract-full').style.display = 'none'; document.getElementById('2112.11514v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">https://www.sciencedirect.com/science/article/abs/pii/S0885230821001169</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Elsevier Computer Speech and Language, Volume 72, March 2022 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.04374">arXiv:2002.04374</a> <span> [<a href="https://arxiv.org/pdf/2002.04374">pdf</a>, <a href="https://arxiv.org/format/2002.04374">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-030-33904-3_66">10.1007/978-3-030-33904-3_66 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=V%C3%A1squez-Correa%2C+J+C">J. C. V谩squez-Correa</a>, <a href="/search/cs?searchtype=author&query=Arias-Vergara%2C+T">T. Arias-Vergara</a>, <a href="/search/cs?searchtype=author&query=Rios-Urrego%2C+C+D">C. D. Rios-Urrego</a>, <a href="/search/cs?searchtype=author&query=Schuster%2C+M">M. Schuster</a>, <a href="/search/cs?searchtype=author&query=Rusz%2C+J">J. Rusz</a>, <a href="/search/cs?searchtype=author&query=Orozco-Arroyave%2C+J+R">J. R. Orozco-Arroyave</a>, <a href="/search/cs?searchtype=author&query=N%C3%B6th%2C+E">E. N枚th</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.04374v1-abstract-short" style="display: inline;"> Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.04374v1-abstract-full').style.display = 'inline'; document.getElementById('2002.04374v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.04374v1-abstract-full" style="display: none;"> Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8\% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.04374v1-abstract-full').style.display = 'none'; document.getElementById('2002.04374v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In Iberoamerican Congress on Pattern Recognition (pp. 697-706) 2019 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 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