Emotion recognition from speech using source, system, and prosodic features
International Journal of Speech Technology
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A novel text independent speaker identification system is proposed. In the proposed system, the 12-order perceptual linear predictive cepstrum and their delta coefficients in the span of five frames are extracted from segmented speech based on the method of pitch synchronous analysis. The Fisher ratio is used to evaluate the effectiveness of speech feature and select the part dimensions of the original 25-dimensional feature vector to form the new 13-dimensional feature vector. The Gaussian Mixture Model is applied to model the speakers. The experimental results show that the proposed system gives very good performances, which the identification accuracy is significantly better than that of the other 13-dimensional feature based systems and is a little bit better than or just the same as the 25-dimensional feature based system, but the algorithm complexity is much less than that of the 25-dimensional features based system.