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- W29375371 abstract "In this paper, we propose a word boundary unconstraint Viterbi algorithm for robust speech recognition against endpoint detection error on isolated word task domain. In general, onekeyword spotting framework is used to achieve robust recognition by absorbing non-speech events with acoustic filler models. One drawback of such an approach is that there is little improvement or it can even hurt performance if unexpected non-speech events occur, whose spectral characteristics are not trained into acoustic filler models. However, it is unrealistic to prepare acoustic filler models considering all kinds of non-speech events especially occurring in mobile environment. Therefore, we propose another approach where non-speech events are absorbed implicitly by relaxing endpoints constraint of Viterbi algorithm. The experimental results show that the algorithm reduces the word error rate from 80.2% to 10.6% for inaccurately endpoint-detected utterances while consuming a little more computation. Index Terms — Keyword-spotting, acoustic filler model, Viterbi decoding" @default.
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- W29375371 date "2009-10-04" @default.
- W29375371 modified "2023-09-23" @default.
- W29375371 title "Word Boundary Unconstraint Viterbi Search For Robust Speech Recognition" @default.
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