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Statistical parametric speech synthesis using conversational data and phenomena
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Speaker similarity evaluation of foreign-accented speech synthesis using HMM-based speaker adaptation
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Speaker adaptation and the evaluation of speaker similarity in the EMIME speech-to-speech translation project
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In: http://infoscience.epfl.ch/record/150620 (2010)
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Speaker adaptation and the evaluation of speaker similarity in the EMIME speech-to-speech translation project
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Speaker adaptation and the evaluation of speaker similarity in the EMIME speech-to-speech translation project
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Speech production knowledge in automatic speech recognition
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An elitist approach to automatic articulatory-acoustic feature classification for phonetic characterization of spoken language.
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Asynchronous Articulatory Feature Recognition Using Dynamic Bayesian networks
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Abstract:
This paper builds on previous work where dynamic Bayesian networks (DBN) were proposed as a model for articulatory feature recognition. Using DBNs makes it possible to model the dependencies between features, an addition to previous approaches which was found to improve feature recognition performance. The DBN results were promising, giving close to the accuracy of artificial neural nets (ANNs). However, the system was trained on canonical labels, leading to an overly strong set of constraints on feature co-occurrence. In this study, we describe an embedded training scheme which learns a set of data-driven asynchronous feature changes where supported in the data. Using a subset of the OGI Numbers corpus, we describe articulatory feature recognition experiments using both canonically-trained and asynchronous-feature DBNs. Performance using DBNs is found to exceed that of ANNs trained on an identical task, giving a higher recognition accuracy. Furthermore, inter-feature dependencies result in a more structured model, giving rise to fewer feature combinations in the recognition output. In addition to an empirical evaluation of this modeling approach, we give a qualitative analysis, investigating the asynchrony found through our data-driven method and interpreting it using linguistic knowledge.
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Keyword:
Articulatory feature recognition; dynamic Bayesian networks
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URL: http://hdl.handle.net/1842/923
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On the Articulatory Representation of Speech within the Evolving Transformation System Formalism
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Syllable Classification Using Articulatory-Acoustic Features
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Wester, Mirjam. - : International Speech Communication Association, 2003
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