Dysarthric speech recognition using dysarthria-severity-dependent and speaker-adaptive models

Dysarthria is a motor speech disorder that impairs the physical production of speech. Modern automatic speech recognition for normal speech is ineffective for dysarthric speech due to the large mismatch of acoustic characteristics. In this paper, a new speaker adaptation scheme is proposed to reduce the mismatch. First, a speaker with dysarthria is classified into one of the pre-defined severity-levels, and then an initial model to be adapted is selected depending on their severity-level. The candidates of an initial model are generated using dysarthric speech associated with their labeled severity-level in the training phase. Finally, speaker adaptation is applied to the selected initial model. Evaluation of the proposed method on a database of several hundred words for 31 speakers with moderate to mild dysarthria showed that the proposed approach provides substantial improvement over the conventional speaker-adaptive system when a small amount of adaptation data is available.