GIF-SP: GA-based informative feature for noisy speech recognition

Satoshi Tamura*, Yoji Tagami, Satoru Hayamizu

*この研究の対応する著者

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

This paper proposes a novel discriminative feature extraction method. The method consists of two stages; in the first stage, a classifier is built for each class, which categorizes an input vector into a certain class or not. From all the parameters of the classifiers, a first transformation can be formed. In the second stage, another transformation that generates a feature vector is subsequently obtained to reduce the dimension and enhance recognition ability. These transformations are computed applying genetic algorithm. In order to evaluate the performance of the proposed feature, speech recognition experiments were conducted. Results in clean training condition shows that GIF greatly improves recognition accuracy compared to conventional MFCC in noisy environments. Multi-condition results also clarifies that out proposed scheme is robust against differences of conditions.

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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