Techniques for task independent word spotting in continuous speech messages

The Lincoln hidden Markov model (HMM)-based word spotting system has demonstrated good performance in spotting keywords in completely unconstrained continuous speech utterances (see R.C. Rose and D.B. Paul, 1990). The word spotter has been evaluated under a number of scenarios, and has been integrated into a system that performs the higher level of classifying conversational speech messages according to topic. In all of these scenarios, anywhere from 25 to 78 examplars per keyword have been used to train the subword acoustic HMMs that are used in the word spotter. In most word spotting applications it is simply not possible to collect such a large number of spoken utterances for all the keywords in the vocabulary every time the system is to be reconfigured for a given task. Therefore, it is essential that techniques be developed to reduce the amount of task-specific speech data required for training HMM-based work spotters.<<ETX>>