We investigated the speech recognition of a person with articulation disorders resulting from athetoid cerebral palsy. Recently, the accuracy of speaker-independent speech recognition has been remarkably improved by the use of stochastic modeling of speech. However, the use of those acoustic models causes degradation of speech recognition for a person with different speech styles (e.g., articulation disorders). In this paper, we discuss our efforts to build an acoustic model for a person with articulation disorders. The articulation of the first speech tends to become unstable due to strain on muscles and that causes degradation of speech recognition. Therefore, we propose a robust feature extraction method based on PCA (Principal Component Analysis) instead of MFCC. Its effectiveness is confirmed by word recognition experiments.
Bibliographic reference. Matsumasa, Hironori / Takiguchi, Tetsuya / Ariki, Yasuo / Li, Ichao / Nakabayashi, Toshitaka (2007): "PCA-based feature extraction for fluctuation in speaking style of articulation disorders", In INTERSPEECH-2007, 1150-1153.