14. A speech recognition system comprising: a speech input signal feature extractor to provide parameters representing features of T groups of N speech input signal frames; a vocabulary of u words; a matrix quantizer to receive the parameters and to provide (i) a series of observation sequences for each of the T groups of the N speech input signal frames and (ii) distance measure output data between the parameters and u respective matrix codebooks; a plurality of u hidden Markov models coupled to the matrix quantizer to receive the observation sequences; a Viterbi algorithm module to receive the observation sequences and provide respective probabilities that the respective hidden Markov models produced a respective observation sequence; a selection control circuit to determine when the distance measure output, the probabilities, and a combination of the distance measure output and the probabilities are included in a plurality of selected mixes by determining an appropriate balance between speech recognition accuracy of the speech recognition system and a speech recognition processing speed of the speech recognition system; a mixer coupled to the matrix quantizer and the Viterbi algorithm module for mixing the distance measure output and the probabilities into one set of mixed output data based on the selected mixes; and a neural network coupled to the mixer to receive the mixed output data set and determine which of the u vocabulary words most probably represents the speech input signal.
14. A speech recognition system comprising: a speech input signal feature extractor to provide parameters representing features of T groups of N speech input signal frames; a vocabulary of u words; a matrix quantizer to receive the parameters and to provide (i) a series of observation sequences for each of the T groups of the N speech input signal frames and (ii) distance measure output data between the parameters and u respective matrix codebooks; a plurality of u hidden Markov models coupled to the matrix quantizer to receive the observation sequences; a Viterbi algorithm module to receive the observation sequences and provide respective probabilities that the respective hidden Markov models produced a respective observation sequence; a selection control circuit to determine when the distance measure output, the probabilities, and a combination of the distance measure output and the probabilities are included in a plurality of selected mixes by determining an appropriate balance between speech recognition accuracy of the speech recognition system and a speech recognition processing speed of the speech recognition system; a mixer coupled to the matrix quantizer and the Viterbi algorithm module for mixing the distance measure output and the probabilities into one set of mixed output data based on the selected mixes; and a neural network coupled to the mixer to receive the mixed output data set and determine which of the u vocabulary words most probably represents the speech input signal. 15. The speech recognition system of claim 14 wherein the matrix quantizer is a fuzzy matrix quantizer, the distance measures are fuzzy distance measures, and the observation sequence is a vector of indices representing the relative closeness of each of the parameters and codewords in the respective matrix codebooks.