Similar speaker recognition using nonlinear analysis

Speech features of the conventional speaker identification system, are usually obtained by linear methods in spectral space. However, these methods have the drawback that speakers with similar voices cannot be distinguished, because the characteristics of their voices are also similar in spectral space. To overcome the difficulty in linear methods, we propose to use the correlation exponent in the nonlinear space as a new feature vector for speaker identification among persons with similar voices. We show that our proposed method surprisingly reduces the error rate of speaker identification system to speakers with similar voices.