Acoustic features for estimation of perceptional similarity

Yoshihiro Adachi*, Shinichi Kawamoto, Shigeo Morishima, Satoshi Nakamura

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2007 - 8th Pacific Rim Conference on Multimedia, Proceedings
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783540772545
Publication statusPublished - 2007
Externally publishedYes
Event8th Pacific-Rim Conference on Multimedia, PCM 2007 - Hong Kong, Hong Kong
Duration: 2007 Dec 112007 Dec 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4810 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th Pacific-Rim Conference on Multimedia, PCM 2007
Country/TerritoryHong Kong
CityHong Kong


  • Acoustic features
  • Perceptional similarity
  • Physical similarity
  • Spearman's rank correlation coefficient

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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