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In this paper we develop an approach to automatic, data-driven generation of pronunciation dictionaries for keyword spotting(KWS) systems. In practical applications, KWS tasks often have to deal with keywords whose pronunciations can not be found in the dictionary. To solve this problem, we study how to derive
Keyword spotting is the task of detecting keywords of interest in continuous speech. This work investigates the application of keyword spotting to detect crime and it can be used along with telephone tapping and audio monitoring devices by security organization. In this work phonetic based word spotter is developed. A
This paper proposes an emotion classification method for spoken utterances using a spoken-term detection (STD) method. This is a keyword extraction method using spoken utterances. The extracted keywords are used to decide on the emotion category of an utterance. Most keywords extracted by the STD system are redundant
measure. We evaluate the system performance in keyword recognition on the small vocabulary track of the 2nd CHiME Challenge and connected digit recognition on the AURORA-2 database. The results show that the proposed system achieves comparable results with state-of-the-art noise robust recognition systems.
express an unified meaning. What's more, university BBS has its own characteristics and forum posts are colloquial, the existing topic model is unsuitable. Based on the results of LDA (Latent Dirichlet Allocation), this paper proposed a topic label extraction method, including three steps, as topic modeling, keywords
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