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A discriminative training method incorporating pronunciation variations for dysarthric automatic speech recognition

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Abstract
While dysarthric speech recognition can be a convenient interface for dysarthric speakers, it is hard to collect enough speech data to overcome the underestimation problem of acoustic models. In addition, there are lots of pronunciation variations in the collected database due to the paralysis of the articulator of dysarthric speakers. Thus, a discriminative training method is proposed for improving the performance of such resource-limited dysarthric speech recognition. The proposed method is applied to subspace Gaussian mixture modeling by incorporating pronunciation variations into a conventional minimum phone error discriminative training method.
Author(s)
Seong, Woo KyeongKim, Nam KyunHa, Hun KyuKim, Hong Kook
Issued Date
2016-12
Type
Conference Paper
DOI
10.1109/APSIPA.2016.7820840
URI
https://scholar.gist.ac.kr/handle/local/20463
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Conference Place
KO
Appears in Collections:
Department of Electrical Engineering and Computer Science > 2. Conference Papers
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