Nonlinear Dynamics In Voiceprint Analysis

Along with the development of information technology and network communications, information security which become a new problem of the Information Age is cared increasingly by people. Like dactylogram, speech character can be used to identify different speakers. Today speech recognition technology have been obtained great development but it is not ideal still. The mam reason is that speech signal is a nonlinear time serial and lacks a useful theoretic tool to analyze it .In recent years, the concept of complexity provoked interest; and applying complexity to nonlinear system analysis has been making gratifying achievements.Usually linear techniques such as spectrum analysis were used to analyze the speech signal. As these traditional linear techniques are merely applicable to the steady, coherent and balanced linear time series, they generally ignored the most important information, which contained the essence of the unsteady, incoherent and unbalanced nonlinear time series of speech. So we try to analyze the speech character using complexity, we find the complexity for speech character of individual testee differs with others even if testees speakthe same sentences.We have applied many complexity measures and theory of statistics to analyzing many speech signal. Had analyzed these algorithms, we bring forward a new complexity measure: partition measure complexity to overcome the disadvantage of those older methods. We had tested its advantage through the test. We conclude that this analysis method for tone texture can be applied to analyze speech character and this technique has wide application prospect.