Cepstral Amplitude Range Normalization for Noise Robust Speech Recognition

Shingo YOSHIZAWA  Noboru HAYASAKA  Naoya WADA  Yoshikazu MIYANAGA  

IEICE TRANSACTIONS on Information and Systems   Vol.E87-D   No.8   pp.2130-2137
Publication Date: 2004/08/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech and Hearing
speech recognition,  robust features,  cepstrum,  Noisex92,  

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This paper describes a noise robustness technique that normalizes the cepstral amplitude range in order to remove the influence of additive noise. Additive noise causes speech feature mismatches between testing and training environments and it degrades recognition accuracy in noisy environments. We presume an approximate model that expresses the influence by changing the amplitude range and the DC component in the log-spectra. According to this model, we propose a cepstral amplitude range normalization (CARN) that normalizes the cepstral distance between maximum and minimum values. It can estimate noise robust features without prior knowledge or adaptation. We evaluated its performance in an isolated word recognition task by using the Noisex92 database. Compared with the combinations of conventional methods, the CARN could improve recognition accuracy under various SNR conditions.