Acoustic features for estimation of perceptional similarity

Yoshihiro Adachi, Shinichi Kawamoto, Shigeo Morishima, Satoshi Nakamura

研究成果: Conference contribution

抜粋

This paper describes an examination of acoustic features for the estimation of perceptional similarity between speeches. We firstly extract some acoustic features including personality from speeches of 36 persons. Secondly, we calculate each distance between extracted features using Gaussian Mixture Model (GMM) or Dynamic Time Warping (DTW), and then we sort speeches based on the physical similarity. On the other hand, there is the permutation based on the perceptional similarity which is sorted according to the subject. We evaluate the physical features by the Spearman's rank correlation coefficient with two permutations. Consequently, the results show that DTW distance with high STRAIGHT Cepstrum is an optimum feature for estimation of perceptional similarity.

元の言語English
ホスト出版物のタイトルAdvances in Multimedia Information Processing - PCM 2007 - 8th Pacific Rim Conference on Multimedia, Proceedings
ページ306-314
ページ数9
出版物ステータスPublished - 2007 12 1
外部発表Yes
イベント8th Pacific-Rim Conference on Multimedia, PCM 2007 - Hong Kong, Hong Kong
継続期間: 2007 12 112007 12 14

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4810 LNCS
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Conference

Conference8th Pacific-Rim Conference on Multimedia, PCM 2007
Hong Kong
Hong Kong
期間07/12/1107/12/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Adachi, Y., Kawamoto, S., Morishima, S., & Nakamura, S. (2007). Acoustic features for estimation of perceptional similarity. : Advances in Multimedia Information Processing - PCM 2007 - 8th Pacific Rim Conference on Multimedia, Proceedings (pp. 306-314). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 4810 LNCS).