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Universal Access: Experiments in Automatic Recognition of Dysarthric Speech By

This thesis describes the results of first experiments in small and medium vocabulary dysarthric speech recognition, using the database being recorded by the Statistical Speech Technology Group under the Universal Access initiative. Speaker-dependent, wordand phone-level speech recognizers utilizing the hidden Markov model architecture were developed and tested; the models were trained exclusively on dysarthric speech produced by individuals diagnosed with cerebral palsy. The experiments indicate that (a) different system configurations (being word vs. phone based, number of states per HMM, number of Gaussian components per state-specific observation probability density, etc.) give useful performance (in terms of recognition accuracy) for different speakers and different task vocabularies, and (b) for subjects with very low intelligibility, speech recognition outperforms human listeners on recognizing dysarthric speech.