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- W2134998712 abstract "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." @default.
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- W2134998712 date "2002-11-07" @default.
- W2134998712 modified "2023-10-03" @default.
- W2134998712 title "Feature extraction using discrete wavelet transform for speech recognition" @default.
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- W2134998712 doi "https://doi.org/10.1109/secon.2000.845444" @default.
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