Research On Speaker Identification In Noisy Environment

Speaker recognition is task of identifying or verifying who is speaking by analyzing and recognizing specific information abstracted from speech waves of speaker. Vector Quantization is one of popular methods for text-independent speaker identification at current. In the process of Vector Quantization, traditional LBG algorithm owns the advantage of fast convergence, but it is easy to get the local optimal result, so the codebook designed by LBG is not optimal and recognition performance will be influenced. According to the understanding that Genetic Algorithm has the capability of getting the global optimal result, a hybrid clustering method GA-K based on Genetic Algorithm and K-means algorithm is proposed to improve the codebook quality. Some feature parameters such as LPCC and MFCC are analyzed with GA-K in identification experiments via test voice utterance length. Experiments show the proposed GA-K method is effective. A new robust feature-extraction algorithm based on wavelet transform is proposed in this paper, because a speaker recognition system with high performance in clean environment will become deficient with unacceptable recognition performance in noisy environment. Benefit from its multi-resolution analysis abilities, the cepstrum features detected from several different time-frequency channels are integrated with a statistical entropy values. Experiments show that the proposed algorithm is quite efficient for speaker identification in noisy environment.