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Feature extraction using discrete wavelet transform for speech recognition

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2 Author(s)
Z. Tufekci ; Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA ; J. N. Gowdy

We propose a new feature vector consisting of mel-frequency discrete wavelet coefficients (MFDWC). The MFDWC are obtained by applying the discrete wavelet transform (DWT) to the mel-scaled log filterbank energies of a speech frame. The purpose of using the DWT is to benefit from its localization property in the time and frequency domains. MFDWC are similar to subband-based (SUB) features and multi-resolution (MULT) features in that both attempt to achieve good time and frequency localization. However, MFDWC have better time/frequency localization than SUB features and MULT features. We evaluated the performance of new features for clean speech and noisy speech and compared the performance of MFDWC with mel-frequency cepstral coefficients (MFCC), SUB features and MULT features. Experimental results on a phoneme recognition task showed that a MFDWC-based recognizer gave better results than recognizers based on MFCC, SUB features, and MULT features for white Gaussian noise, band-limited white Gaussian noise and clean speech cases

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Southeastcon 2000. Proceedings of the IEEE

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