|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
FAST IMPLEMENTATIONS OF VITERBI-BASED WORD-SPOTTING
Kate Knill and Steve Young
This paper explores methods of increasing the speed of a Viterbi-based word-spotting system for audio document retrieval. Fast processing is essential since the user expects to receive the results of a keyword search many times faster than the actual length of the speech. A number of computational short-cuts to the standard Viterbi word-spotter are presented. These are based on exploiting the background Viterbi phone recognition path that is computed to provide a normalisation base. An initial approximation using the phone transition boundaries reduces the retrieval time by a factor of 5, while achieving a slight improvement in word-spotting performance. To further reduce retrieval time, pattern matching, feature selection, and Gaussian selection techniques are applied to this approximate pass to give a total x50 increase in speed with little loss in performance. In addition, a low memory requirement means that these approaches can be implemented on any platform, including hand-held devices.
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