To improve the performance of speaker identification systems, an effective
and robust method is proposed to extract speech features, capable of operating
in noisy environment. Based on the time-frequency multi-resolution property of
wavelet transform, the input speech signal is decomposed into various frequency
channels. For capturing the characteristic of the signal, the Mel-Frequency
Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. Hidden
Markov Models (HMMs) were used for the recognition stage as they give better
recognition for the speaker's features than Dynamic Time Warping (DTW).
Comparison of the proposed approach with the MFCCs conventional feature
extraction method shows that the proposed method not only effectively reduces
the influence of noise, but also improves recognition. A recognition rate of
99.3% was obtained using the proposed feature extraction technique compared to
98.7% using the MFCCs. When the test patterns were corrupted by additive white
Gaussian noise with 20 dB S/N ratio, the recognition rate was 97.3% using the
proposed method compared to 93.3% using the MFCCs.