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

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

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

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

Electrical and Computer Engineering

Producción científica: Conference contribution › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers
Páginas	277-283
Número de páginas	7
Estado	Published - 2005
Evento	International Conference on Non-Linear Speech Processing, NOLISP 2005 - Barcelona, Spain Duración: abr 19 2005 → abr 22 2005

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	3817 LNAI
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conference

Conference	International Conference on Non-Linear Speech Processing, NOLISP 2005
País/Territorio	Spain
Ciudad	Barcelona
Período	4/19/05 → 4/22/05

Nota bibliográfica

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

Otros archivos y enlaces

Citar esto

Indrebo, K. M., Povinelli, R. J., & Johnson, M. T. (2005). Third-order moments of filtered speech signals for robust speech recognition. En Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers (pp. 277-283). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3817 LNAI).

Indrebo, Kevin M. ; Povinelli, Richard J. ; Johnson, Michael T. / Third-order moments of filtered speech signals for robust speech recognition. Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers. 2005. pp. 277-283 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Third-order moments of filtered speech signals for robust speech recognition",

abstract = "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.",

author = "Indrebo, \{Kevin M.\} and Povinelli, \{Richard J.\} and Johnson, \{Michael T.\}",

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.; International Conference on Non-Linear Speech Processing, NOLISP 2005 ; Conference date: 19-04-2005 Through 22-04-2005",

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booktitle = "Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers",

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Indrebo, KM, Povinelli, RJ & Johnson, MT 2005, Third-order moments of filtered speech signals for robust speech recognition. En Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3817 LNAI, pp. 277-283, International Conference on Non-Linear Speech Processing, NOLISP 2005, Barcelona, Spain, 4/19/05.

Third-order moments of filtered speech signals for robust speech recognition. / Indrebo, Kevin M.; Povinelli, Richard J.; Johnson, Michael T.
Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers. 2005. p. 277-283 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3817 LNAI).

Producción científica: Conference contribution › revisión exhaustiva

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AU - Indrebo, Kevin M.

AU - Povinelli, Richard J.

AU - Johnson, Michael T.

N1 - 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.

PY - 2005

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Indrebo KM, Povinelli RJ, Johnson MT. Third-order moments of filtered speech signals for robust speech recognition. En Nonlinear Analyses and Algorithms for Speech Processing - International Conference on Non-Linear Speech Processing, NOLISP 2005, Revised Selected Papers. 2005. p. 277-283. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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Nota bibliográfica

ASJC Scopus subject areas

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