On the use of hidden Markov models for speaker‐independent recognition of isolated words from a medium size vocabulary

Abstract

Recent work at Bell Laboratories has shown how the theories of LPC Vector Quantization (VQ) and hidden Markov modeling (HMM) can be applied to the recognition of isolated word vocabularies. Our first experiments with HMM based recognizers were restricted to a vocabulary of the ten digits. For this simple vocabulary we found that a high performance recognizer (word accuracy on the order of 97%) could be implemented, and that the performance was, for the most part, insensitive to parameters of both the Markov model and the vector quantizer. In this talk we extend our investigations to the recognition of isolated words from a medium size vocabulary, (129 words), as used in the Bell Laboratories airline reservation and information system. For this moderately complex vocabulary we have found that recognition accuracy is indeed a function of the HMM parameter (i.e., the number of states and the number of symbols in the vector quantizer). We have also found that a vector quantizer which uses energy information gives better performance than a conventional LPC shape vector quantizer of the same size (i.e., number of codebook entries).