Combination of autocorrelation-based features and projection measure technique for speaker identification

Abstract

This paper presents a robust approach for speaker identification when the speech signal is corrupted by additive noise and channel distortion. Robust features are derived by assuming that the corrupting noise is stationary and the channel effect is fixed during an utterance. A two-step temporal filtering procedure on the autocorrelation sequence is proposed to minimize the effect of additive and convolutional noises. The first step applies a temporal filtering procedure in autocorrelation domain to remove the additive noise, and the second step is to perform the mean subtraction on the filtered autocorrelation sequence in logarithmic spectrum domain to remove the channel effect. No prior knowledge of noise characteristic is necessary. The additive noise can be a colored noise. Then the proposed robust feature is combined with the projection measure technique to gain further improvement in recognition accuracy. Experimental results show that the proposed method can significantly improve the performance of speaker identification task in noisy environment.