Filter Bank Subtraction for Robust Speech Recognition

Kazuo ONOE  Hiroyuki SEGI  Takeshi KOBAYAKAWA  Shoei SATO  Shinichi HOMMA  Toru IMAI  Akio ANDO  

IEICE TRANSACTIONS on Information and Systems   Vol.E86-D   No.3   pp.483-488
Publication Date: 2003/03/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Speech Information Processing)
Category: Robust Speech Recognition and Enhancement
filter bank,  spectral subtraction,  speech recognition,  noise,  

Full Text: PDF>>
Buy this Article

In this paper, we propose a new technique of filter bank subtraction for robust speech recognition under various acoustic conditions. Spectral subtraction is a simple and useful technique for reducing the influence of additive noise. Conventional spectral subtraction assumes accurate estimation of the noise spectrum and no correlation between speech and noise. Those assumptions, however, are rarely satisfied in reality, leading to the degradation of speech recognition accuracy. Moreover, the recognition improvement attained by conventional methods is slight when the input SNR changes sharply. We propose a new method in which the output values of filter banks are used for noise estimation and subtraction. By estimating noise at each filter bank, instead of at each frequency point, the method alleviates the necessity for precise estimation of noise. We also take into consideration expected phase differences between the spectra of speech and noise in the subtraction and control a subtraction coefficient theoretically. Recognition experiments on test sets at several SNRs showed that the filter bank subtraction technique improved the word accuracy significantly and got better results than conventional spectral subtraction on all the test sets. In other experiments, on recognizing speech from TV news field reports with environmental noise, the proposed subtraction method yielded better results than the conventional method.