On the Use of Hidden Markov Models for Speaker-Independent Recognition of Isolated Words From a Medium-Size Vocabulary

Recent work at AT&T Bell Laboratories has shown how the theories of Vector Quantization (VQ) and Hidden Markov Modeling (HMM) can be applied to the recognition of isolated word vocabularies. The initial experiments with an HMM word recognizer were restricted to a vocabulary of 10 digits. For this simple vocabulary with dialed-up telephone recordings, we found that a high-performance, speaker-independent word recognizer could be implemented, and that the performance was, for the most part, insensitive to parameters of both the HMM and the VQ. In this paper we extend our investigations of the HMM recognizer to the recognition of isolated words from a medium-size vocabulary (129 words), as used in the AT&T Bell Laboratories airlines reservation and information system. For this moderately complex word vocabulary, we have found that recognition accuracy is indeed a function of the HMM parameters (i.e., the number of states in the model and the number of symbols per state). We have also found that a VQ that includes energy information gives better performance than a conventional VQ of the same size (i.e., same number of code-book entries).