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.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.