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Bi-Spectral Acoustic Features for Robust Speech Recognition
Kazuo ONOE Shoei SATO Shinichi HOMMA Akio KOBAYASHI Toru IMAI Tohru TAKAGI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2008/03/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: Special Section LETTER (Special Section on Robust Speech Processing in Realistic Environments)
bi-spectrum , non-Gaussianity, phase information, speech recognition,
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The extraction of acoustic features for robust speech recognition is very important for improving its performance in realistic environments. The bi-spectrum based on the Fourier transformation of the third-order cumulants expresses the non-Gaussianity and the phase information of the speech signal, showing the dependency between frequency components. In this letter, we propose a method of extracting short-time bi-spectral acoustic features with averaging features in a single frame. Merged with the conventional Mel frequency cepstral coefficients (MFCC) based on the power spectrum by the principal component analysis (PCA), the proposed features gave a 6.9% relative lower a word error rate in Japanese broadcast news transcription experiments.