8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

PCA-Based Feature Extraction for Fluctuation in Speaking Style of Articulation Disorders

Hironori Matsumasa (1), Tetsuya Takiguchi (1), Yasuo Ariki (1), Ichao Li (2), Toshitaka Nakabayashi (1)

(1) Kobe University, Japan
(2) Otemon Gakuin University, Japan

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.

Full Paper

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.