Prosody-Dependent Acoustic Modeling Using Variable-Parameter Hidden Markov Models

Jui Ting Huang, Po-Sen Huang, Yoonsook Mo, Mark Hasegawa-Johnson, Jennifer S Cole

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As an effort to make prosody useful in spontaneous speech
recognition, we adopt a quasi-continuous prosodic annotation
and accordingly design a prosody-dependent acoustic model to
improve ASR performances. We propose a variable-parameter
Hidden Markov Models, modeling the mean vector as a function of the prosody variable through a polynomial regression
model. The prosodically-adapted acoustic models are used to
re-score the N-best output from a standard ASR, according to
the prosody variable assigned by an automatic prosody detector.
Experiments on the Buckeye corpus demonstrate the effectiveness of our approach.
Original languageEnglish (US)
Title of host publicationProceedings of Speech Prosody 2010
StatePublished - 2010

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  • Cite this

    Huang, J. T., Huang, P-S., Mo, Y., Hasegawa-Johnson, M., & Cole, J. S. (2010). Prosody-Dependent Acoustic Modeling Using Variable-Parameter Hidden Markov Models. In Proceedings of Speech Prosody 2010