Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition
Document Type
Conference Proceeding
Language
eng
Format of Original
7 p.
Publication Date
4-20-2005
Publisher
Springer
Source Publication
International Conference on Non-Linear Speech Processing, NOLISP 2005, Barcelona, Spain, April 19-22, 2005, Revised Selected Papers
Source ISSN
0302-9743
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.
Recommended Citation
Indrebo, Kevin M; Povinelli, Richard J.; and Johnson, Michael T., "Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition" (2005). Electrical and Computer Engineering Faculty Research and Publications. 167.
https://epublications.marquette.edu/electric_fac/167
Comments
Published as part of the proceedings of the International Conference on Non-Linear Speech Processing, NOLISP 2005, Barcelona, Spain, April 19-22, 2005, Revised Selected Papers, 2005: 151-157. DOI.