Speech recognition system and method employing data compression

Abstract

A data compression system greatly compresses the stored data used by a speech recognition system employing hidden Markov models (HMM). The speech recognition system vector quantizes the acoustic space spoken by humans by dividing it into a predetermined number of acoustic features that are stored as codewords in a vector quantization (output probability) table or codebook. For each spoken word, the speech recognition system calculates an output probability value for each codeword, the output probability value representing an estimated probability that the word will be spoken using the acoustic feature associated with the codeword. The probability values are stored in an output probability table indexed by each codeword and by each word in a vocabulary. The output probability table is arranged to allow compression of the probability of values associated with each codeword based on other probability values associated with the same codeword, thereby compressing the stored output probability. By compressing the probability values associated with each codeword separate from the probability values associated with other codewords, the speech recognition system can recognize spoken words without having to decompress the entire output probability table. In a preferred embodiment, additional compression is achieved by quantizing the probability values into 16 buckets with an equal number of probability values in each bucket. By quantizing the probability values into buckets, additional redundancy is added to the output probability table, which allows the output probability table to be additionally compressed.