Third-order moments of filtered speech signals for robust speech recognition

Kevin M. Indrebo, Richard J. Povinelli, Michael T. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise than cepstrals, as Gaussian distributions have zero third-order moments. Experiments on the AURORA2 database studying these features in combination with Mel-frequency cepstral coefficients (MFCC's) are presented, and some improvement over the MFCC-only baseline is shown when clean speech is used for training, though the same improvement is not seen when multi-condition training data is used.

Original languageEnglish
Title of host publicationNonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers
Number of pages7
StatePublished - 2005
EventInternational Conference on Non-Linear Speech Processing, NOLISP 2005 - Barcelona, Spain
Duration: Apr 19 2005Apr 22 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3817 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Non-Linear Speech Processing, NOLISP 2005

Bibliographical note

Funding Information:
The material in this project was prepared under Grant No. SS-41-83-07 from the Social Science Research Council. Researchers undertaking such projects under Council sponsorship are encouraged to express freely their professional judgment. Therefore, the points of view or opinions stated herein do not necessarily represent the official position or policy of the Social Science Research Council.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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