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Adaption Model for Building Agile Pronunciation Dictionaries Using Phonemic Distance Measurements
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<div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87354</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Adaption Model for Building Agile Pronunciation Dictionaries Using Phonemic Distance Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akella%20Amarendra%20Babu">Akella Amarendra Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rama%20Devi%20Yellasiri"> Rama Devi Yellasiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Natukula%20Sainath"> Natukula Sainath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent. <iframe src="https://publications.waset.org/abstracts/93281.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pronunciation%20variations" title="pronunciation variations">pronunciation variations</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming" title=" dynamic programming"> dynamic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a 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