Application of hidden Markov models for recognition of a limited set of words in unconstrained speech

The authors present an algorithm based on hidden Markov models which can recognize a predefined set of vocabulary items spoken in the context of fluent speech. They show that for a vocabulary of five words, it is possible to correctly recognize 87.1% of keywords when they occur in fluent speech and are spoken over a long-distance telephone network. While this task is significantly easier than what is normally associated with keyword spotting in continuous speech, it does address an important problem that must be solved for successful deployment of speech-recognition technology.<>