Feature reconstruction using sparse imputation for noise robust audio-visual speech recognition

Peng Shen*, Satoshi Tamura, Satoru Hayamizu

*この研究の対応する著者

研究成果

2 被引用数 (Scopus)

抄録

In this paper, we propose to use noise reduction technology on both speech signal and visual signal by using exemplar-based sparse representation features for audio-visual speech recognition. First, we introduce sparse representation classification technology and describe how to utilize the sparse imputation to reduce noise not only for audio signal but also for visual signal. We utilize a normalization method to improve the accuracy of the sparse representation classification, and propose a method to reduce the error rate of visual signal when using the normalization method. We show the effectiveness of our proposed noise reduction method and that the audio features achieved up to 88.63% accuracy at -5dB, a 6.24% absolute improvement is achieved over the additive noise reduction method, and the visual features achieved 27.24% absolute improvement at gamma noise.

本文言語English
ホスト出版物のタイトル2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
出版ステータスPublished - 2012
外部発表はい
イベント2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 - Hollywood, CA, United States
継続期間: 2012 12 32012 12 6

出版物シリーズ

名前2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012

Other

Other2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
国/地域United States
CityHollywood, CA
Period12/12/312/12/6

ASJC Scopus subject areas

  • 情報システム

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