Speech Prosody 2010
Chicago, IL, 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
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.