Speaker Recognition On The Base Of Time-Varying Characteristics Of Speech Signal

The speaker recognition is a special kind of speech recognition. In recent years, with the rapid development of technology, the text-dependent speaker verification system has been used in some areas where need identity authentication. But there are still some problems to be solved. One of them is how to reliably describe the speech characteristics for speaker recognition more efficiently.There are speaker verification and speaker identification in speaker recognition. This paper focuses on text-dependent speaker identification. On the base of time-varying characteristics of speech signal, time-varying characteristic frequency (pitch frequency included) is extracted from the average MEL cepstrum, and the cepstrum value series of characteristic frequency are gained on. The deterministic and stochastic fluctuations of the time series are separated by use of time series pretreatment and statistical methods. As zero mean autocovariance nonstationary time series, the stochastic fluctuations are analyzed by the full order TVPAR (Time-Varying Parameter Autoregressive) model, and the characteristic parameters are extracted from speech signals of the speaker. The speech signals are recognized on the stochastic fluctuations of the time series and analysis with the full order TVPAR model.In this paper, the order of regression model is selected by using the minimum BIC (Bayesian Information Criterion) rule, speakers are discriminated by using Mahalanobis distance. The experimental results manifest that the recognition rate obtained by the full order TVPAR model are higher than only on stochastic fluctuations of the time series, with one and two characteristic frequencies, the average recognition rate reaches 98.6% and 100% respectively.