This paper describes the results of our experiments in small and medium vocabulary dysarthric speech recognition, using the database being recorded by our group under the Universal Access initiative. We develop and test speaker-dependent, word- and phone-level speech recognizers utilizing the hidden Markov Model architecture; the models are 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 very low intelligibility subjects, speech recognition outperforms human listeners on recognizing dysarthric speech.
Bibliographic reference. Sharma, Harsh Vardhan / Hasegawa-Johnson, Mark (2009): "Universal access: speech recognition for talkers with spastic dysarthria", In INTERSPEECH-2009, 1451-1454.