ISCA Archive Interspeech 2007
ISCA Archive Interspeech 2007

PCA-based feature extraction for fluctuation in speaking style of articulation disorders

Hironori Matsumasa, Tetsuya Takiguchi, Yasuo Ariki, Ichao Li, Toshitaka Nakabayashi

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.


doi: 10.21437/Interspeech.2007-374

Cite as: Matsumasa, H., Takiguchi, T., Ariki, Y., Li, I., Nakabayashi, T. (2007) PCA-based feature extraction for fluctuation in speaking style of articulation disorders. Proc. Interspeech 2007, 1150-1153, doi: 10.21437/Interspeech.2007-374

@inproceedings{matsumasa07_interspeech,
  author={Hironori Matsumasa and Tetsuya Takiguchi and Yasuo Ariki and Ichao Li and Toshitaka Nakabayashi},
  title={{PCA-based feature extraction for fluctuation in speaking style of articulation disorders}},
  year=2007,
  booktitle={Proc. Interspeech 2007},
  pages={1150--1153},
  doi={10.21437/Interspeech.2007-374}
}