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.

A novel approach to establish membership functions of fuzzy states based on a functional-link neural network (FLNN) for text-independent speaker identification is proposed. Parameters in corresponding states derived from training utterances uttered by each speaker are fed to a fuzzy statistical model to form the histograms of the states. Data in normalized histograms are used as learning samples for the FLNNs. A set of FLNNs is used as a speaker's model. For each unknown speaker's voice in the test, parameters are fed to every FLNN corresponding to each fuzzy state respectively, the FLNN generalizes the degree of membership to the state, then the system scores the degree of membership to the speaker's model and chooses the speaker whose model's degree of membership is the highest.

The lattice-structured adaptive noise canceller has been studied: a brief formula about the relation of its misadjustment to the step size and the number of stages has been found theoretically and checked experimentally, showing that the misadjustment increases exponentially with the number of stages, an optimized step size has been applied to multi-stage lattice filter, with drastic reduction in convergence time.