Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

Learning Statistically Characterized Resonance Targets in a Hidden Trajectory Model of Speech Coarticulation and Reduction

Li Deng, Dong Yu, Alex Acero

Microsoft Research, Redmond, WA, USA

We report our new development of a hidden trajectory model for co-articulated, time-varying patterns of speech. The model uses bi-directional filtering of vocal tract resonance targets to jointly represent contextual variation and phonetic reduction in speech acoustics. A novel maximum-likelihood-based learning algorithm is presented that accurately estimates the distributional parameters of the resonance targets. The results of the estimates are analyzed and shown to be consistent with all the relevant acousticphonetic facts and intuitions. Phonetic recognition experiments demonstrate that the model with more rigorous target training outperforms the most recent earlier version of the model, producing 17.5% fewer errors in N-best rescoring.

Full Paper

Bibliographic reference.  Deng, Li / Yu, Dong / Acero, Alex (2005): "Learning statistically characterized resonance targets in a hidden trajectory model of speech coarticulation and reduction", In INTERSPEECH-2005, 1097-1100.