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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 speech production features. We show that with very few parameters, these features can be tracked, and phoneme sequences recovered, with promising accuracy.