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- W119841344 abstract "This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability 1 along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a time normalization of the resulting score. This makes solutions complex (i.e., LN 2 2 basic operations for keyword HMM models withL states and utterances withN frames). We present here two alternative (quite simple and efcient) solutions to this problem. a) First we provide a method that nds the optimal segmentation according to the criteria of maximizing the average observation probability. It uses Dynamic Programming as a step, but does not require scoring for all possible begin/endpoints. While the worst case remains O(LN 2 ), this technique converged in at most 3(L+2)N basic operations in each experiment for two very different applications. b) The second proposed algorithm does not provide a segmentation but can be used for the decision problem of whether the utterance should be classied as containing the keyword or not (provided a predened threshold on the acceptable average observation probability). This allows the algorithm to be even faster, with x cost of (L+2)N ." @default.
- W119841344 created "2016-06-24" @default.
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- W119841344 date "2005-01-01" @default.
- W119841344 modified "2023-09-27" @default.
- W119841344 title "Iterative Segmentation in Keyword Spotting: relation to fractional programming" @default.
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