Binary quantization of feature vectors for robust text-independent speaker identification

We present a novel approach to vector quantization in which a feature vector is represented by a binary vector. It is called binary quantization (BQ). The performance criterion of vector quantization, distortion (distance) measure, was employed for investigating the effectiveness of BQ. At 12 b/analysis frame, the average distortion caused by BQ is even lower than the intraspeaker average distance between two repetitions of the same word (after DTW alignment). Since the output of BQ is a binary sequence, it is possible to combine it with a forward Hamming net classifier. In terms of the idea of a hierarchical model for describing a speaker individual characteristics, a text-independent speaker identification system was set up. Experimental results show that the performance of this system is very good. Not only are the small memory space and little computation required, in the speaker identification system, but, more importantly, it shows strong robustness in additive Gaussian white noise