An approach to speaker identification using multiple classifiers

The presented paper is interested in a speaker identification problem. The attributes representing the voice of a particular speaker are obtained from very short segments of the speech waveform corresponding only to one pitch period of vowels. The patterns formed from the samples of a pitch period waveform are either matched in the time domain by use of a nonlinear time warping method, known as dynamic time warping (DTW), or they are converted into cepstral coefficients and compared using the cepstral distance measure. Since an uttered speech signal usually contains a lot of vowels the techniques using a combination both various classifiers and multiple classifier outputs are considered in the decision making process. Experiments performed for a hundred speakers are described