Speech Prosody 2010

Chicago, IL, USA
May 10-14, 2010

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

Jui-Ting Huang (1), Po-Sen Huang (1), Yoonsook Mo (2), Mark Hasegawa-Johnson (1), Jennifer Cole (2)

(1) Department of Electrical and Computer Engineering; (2) Department of Linguistics; University of Illinois at Urbana-Champaign, USA

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.

Index Terms: Prosody-dependent ASR, variable parameter HMM, re-scoring

Full Paper

Bibliographic reference.  Huang, Jui-Ting / Huang, Po-Sen / Mo, Yoonsook / Hasegawa-Johnson, Mark / Cole, Jennifer (2010): "Prosody-dependent acoustic modeling using variable-parameter hidden Markov models", In SP-2010, paper 623.