Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition

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. Preliminary experiments on the AURORA2 database studying these features in combination with Mel-frequency cepstral coefficients (MFCC’s) are presented, and improvement over the MFCC-only baseline is shown with the combined feature set for several noise conditions.

• Type: Conference
• Year: 2005
• Where: NOLISP
• Authors: Kevin M. Indrebo, Richard J. Povinelli, Michael T. Johnson
• Keywords: Cepstral Coefficients, NOLISP 2005, Signal Processing, Third-order Moments, Third-order Statistics