Purpose: Articulatory information is explored as a means of improving the recognition of dysarthric speech by machine.
Method: Our data is chiefly derived from the TORGO database of dysarthric articulation in which motions of various points in the vocal tract are measured during speech. The first experiment provides a baseline model indicating a relatively low performance with traditional automatic speech recognition (ASR) using only acoustic data from dysarthric individuals. The second experiment uses various measures of entropy (statistical disorder) to determine whether characteristics of dysarthric articulation can reduce uncertainty in features of dysarthric acoustics. These findings lead to the third experiment in which recorded dysarthric articulation is directly encoded into the speech recognition process.
Results: We find that 18.3\% of the statistical disorder in the acoustics of dysarthric speakers can be removed if articulatory parameters are known. Using articulatory models reduces phoneme recognition errors relatively by up to 6\% for dysarthric speakers in speaker-dependent systems.
Conclusions: Articulatory knowledge is useful in reducing rates of error in automatic speech recognition for speakers with dysarthria, and in reducing statistical uncertainty of their acoustic signals. These findings may help to guide clinical decisions related to the use of ASR in the future." }