Variational Inference and Learning for Segmental Switching State Space Models of Hidden Speech Dynamics

H. Attias, L. Lee, and Li Deng

Abstract

This paper describes novel and powerful variational EM al- gorithms for the segmental switching state space models used in speech applications, which are capable of capturing key internal (or hidden) dynamics of natural speech pro- duction. Hidden dynamic models (HDMs) have recently become a class of promising acoustic models to incorporate crucial speech-speci¯c knowledge and overcome many inher- ent weaknesses of traditional HMMs. However, the lack of powerful and e±cient statistical learning algorithms is one of the main obstacles preventing them from being well stud- ied and widely used. Since exact inference and learning are intractable, a variational approach is taken to develop ef- fective approximate algorithms. We have implemented the segmental constraint crucial for modeling speech dynamics and present algorithms for recovering hidden speech dy- namics and discrete speech units from acoustic data only. The e®ectiveness of the algorithms developed are veri¯ed by experiments on simulation and Switchboard speech data.

Details

Publication typeInproceedings
Published inProc. of the Int. Conf. on Acoustics, Speech, and Signal Processing
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