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  • NL Seminar- Jackie Lee: "Bayesian Approaches to Acoustic Model and Pronunciation Lexicon Discovery"

    Fri, Jul 19, 2013 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Jackie Lee, MIT

    Talk Title: "Bayesian Approaches to Acoustic Model and Pronunciation Lexicon Discovery"

    Series: Natural Language Seminar

    Abstract: In the first part of the talk, we investigate the problem of acoustic modeling in which prior language-specific knowledge and transcribed data are unavailable. We present an unsupervised model that simultaneously segments the speech, discovers a proper set of sub-word units (e.g., phones) and learns a Hidden Markov Model (HMM) for each induced acoustic unit. Our approach is formulated as a Dirichlet process mixture model in which each mixture is an HMM that represents a sub-word unit. We apply our model to the TIMIT corpus, and the results demonstrate that our model discovers phone units that are highly correlated with English phones as well as produces better segmentation than the state-of-the-art baselines. We test the quality of the learned acoustic models on a spoken term detection task. Compared to the baseline, our model is able to improve the detection precision of top hits by a large margin.

    The creation of a pronunciation lexicon remains the most inefficient process in developing an automatic speech recognizer. In the second part of the talk, we discuss an unsupervised alternative to the conventional manual approach for creating pronunciation dictionaries. We present a hierarchical Bayesian model, which jointly discovers the phonetic inventory and the Letter-to-Sound (L2S) mapping rules in a language using only transcribed data. When tested on a corpus of spontaneous queries, our results demonstrate the superiority of the proposed joint learning scheme over its sequential counterpart, in which the latent phonetic inventory and L2S mappings are learned separately. Furthermore, the recognizers built with the automatically induced lexicon consistently outperform grapheme-based recognizers and even approach the performance of recognition systems trained using conventional supervised procedures.

    Biography: http://groups.csail.mit.edu/sls/people/clee.shtml

    Host: Qing Dou

    More Info: http://nlg.isi.edu/nl-seminar/

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

    Event Link: http://nlg.isi.edu/nl-seminar/

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