Correlation-Based Similarity Between Signals for Speaker Verification with Limited Amount of Speech Data
In this paper, we present a method for speaker verification with limited amount (2 to 3 secs) of speech data. With the constraint of limited data, the use of traditional vocal tract features in conjunction with statistical models becomes difficult. An estimate of the glottal flow derivative signal which represents the excitation source information is used for comparing two signals. Speaker verification is performed by computing normalized correlation coefficient values between signal patterns chosen around high SNR regions (corresponding to the instants of significant excitation), without having to extract any further parameters. The high SNR regions are detected by locating peaks in the Hilbert envelope of the LP residual signal. Speaker verification studies are conducted on clean microphone speech (TIMIT) as well as noisy telephone speech (NTIMIT), to illustrate the effectiveness of the proposed method.
KeywordsGaussian Mixture Model Vocal Tract Speech Data Equal Error Rate Speaker Recognition
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