|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SPEAKER DEPENDENT KEYWORD SPOTTING FOR ACCESSING STORED SPEECH
Kate Knill and Steve Young
This report investigates the use of a speaker-dependent HMM word-spotter to retrieve spoken messages. The baseline word-spotter consists of a parallel network of keyword and background filler models. A further pass, using the filler models only, can be used to re-score the putative keyword hits. Word-spotting performance using (i) whole-word and (ii) sub-word keyword models is investigated. For each keyword model type, effects on performance of re-scoring, Gaussian component number, and parameter set are evaluated on a set of spoken messages, taken from the Video Mail Retrieval database. Overall, sub-word models are shown to yield a higher hit rate, particularly before the first false alarm occurs.
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