Special feature vector coding and appropriate distance definition developed for a speech recognition system

In this paper a single-word speech-recognition system based on autocorrelation feature vectors is presented. Existing comparable systems use an extra data word for each coefficient of a feature vector. Normally for large vocabularies, vector quantization is performed in order to reduce the resulting large amount of data. Another way to reduce the storage needed is proposed by using a rough vector coefficient quantization instead of vector quantization. If, for example, 16 autocorrelation coefficients coded with two bits each are stored in one 32 bit data word, one obtains, besides an optimal use of the available storage, a good facility of computing a distance between pairs of feature vectors in a very fast way. A modified distance measure based on the cityblock distance is introduced. It only takes about 200 ns to compute one distance with the aid of appropriate programmed read only memories. If coefficient quantization is involved instead of vector quantization and the modified distance measure is used, there is no loss of accuracy. In each case we obtained recognition rates better than 95% for a 250-word recognition system.