Bayesian learning of Gaussian mixture densities for hidden Markov models
HLT '91 Proceedings of the workshop on Speech and Natural Language
MAP estimation of continuous density HMM: theory and applications
HLT '91 Proceedings of the workshop on Speech and Natural Language
Comparing speaker-dependent and speaker-adaptive acoustic models for recognizing dysarthric speech
Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility
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This paper describes the results of our experiments in building speaker-adaptive recognizers for talkers with spastic dysarthria. We study two modifications -- (a) MAP adaptation of speaker-independent systems trained on normal speech and, (b) using a transition probability matrix that is a linear interpolation between fully ergodic and (exclusively) left-to-right structures, for both speaker-dependent and speaker-adapted systems. The experiments indicate that (1) for speaker-dependent systems, left-to-right HMMs have lower word error rate than transition-interpolated HMMs, (2) adapting all parameters other than transition probabilities results in the highest recognition accuracy compared to adapting any subset of these parameters or adapting all parameters including transition probabilities, (3) performing both transition-interpolation and adaptation gives higher word error rate than performing adaptation alone and, (4) dysarthria severity is not a sufficient indicator of the relative performance of speaker-dependent and speaker-adapted systems.