Automatic Speaker Verification System Based on Computation Complexity Features

摘要

The Automatic speaker verification in this paper introduces one novel feature selection and extraction method, computation complexity features. As traditional linear features are mainly based on frequency analysis, and the assumptions used to extract traditional linear features do not describe the nonlinear dynamic evolution of the system, 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. Computation complexity feature can extract that nonlinear character of speech signal, which overcomes the disadvantage of the traditional linear feature extraction method. In this paper, the computation complexity theory is applied to feature extraction of speech signal; this is a creative thought and trial. This automatic speaker verification system mainly included three modules: speech signal preprocess, features extraction (traditional feature and complexity feature), pattern recognition (distance matching). The corpus used in the tests is composed by 50 different speakers. Through the experiments, one conclusion comes that the combination of the nonlinear features and traditional linear features can reduce the verification errors markedly and gain more accurate results.