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This paper presents automatic pronunciation transliteration method with acoustic and contextual analysis for Chinese-English mixed language keyword spotting (KWS) system. More often, we need to develop robust Chinese-English mixed language spoken language technology without Chinese accented English acoustic data. In
a null score to any keyword that was not part of the training data, i.e. Out-of-Vocabulary (OOV) keywords, whereas other techniques are able to estimate a reasonable score even for these kind of keywords. We present a smoothing technique which estimates the score of an OOV keyword based on the scores of similar
The aim of the spoken term detection task is to find the occurrence of user-entered keywords in an archive of audio recordings. In this area, besides the accuracy of hits returned, the speed of search is also very important, for which an intermediate representation of recordings is normally used. In this paper we
to lexical-cohesion-based text segmenters applied to human transcripts. Furthermore, the performance of the text segmenters applied to LVCSR output decreases significantly when keywords are not included in the lexicon. This suggests that, for the purpose of obtaining topical structure, the phoneme sequence segmenter
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