Title: IMPROVED MFCC FEATURE EXTRACTION BY PCA-OPTIMIZED FILTER-BANK FOR SPEECH RECOGNITION

Authors: Shang-Ming Lee, Shi-Hau Fang, Jeih-weih Hung, Lin-Shan Lee

Abstract: Although Mel-frequency Cepstral Coefficients (MFCC) have been proven to perform very well under most conditions, some limited efforts have been made in optimizing the shape of the filters in the filter-bank in the conventional MFCC approach. This paper presents a new feature extraction approach that designs the shapes of the filters in the filter-bank. In this new approach the filter-bank coefficients are data-driven obtained by applying the principal component analysis (PCA) on the FFT spectrum of the training data. The experimental results show that this method is robust under noisy environment and is well additive with other noise-handling techniques.

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