Trajectory analysis of speech using continuous state hidden Markov Models

Many current speech models used in recognition involve thousands of parameters, whereas the mechanisms of speech production are conceptually very simple. We present and evaluate a new continuous state probabilistic model (CS-HMM) for recovering dwell-transition and phoneme sequences from dynamic spe...

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Bibliographic Details
Main Authors: Weber, P., Houghton, S. M., Champion, C. J., Russell, M. J., Jancovic, P.
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Continuous State Hidden Markov Model
Dynamic Features
Hidden Markov models
Probabilistic Model
Production
Speech
Speech Analysis
Speech processing
Speech recognition
Vectors
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Description
Summary:Many current speech models used in recognition involve thousands of parameters, whereas the mechanisms of speech production are conceptually very simple. We present and evaluate a new continuous state probabilistic model (CS-HMM) for recovering dwell-transition and phoneme sequences from dynamic speech production features. We show that with very few parameters, these features can be tracked, and phoneme sequences recovered, with promising accuracy.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854159