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Most traditional template matching based keyword recognition methods don't need training data, just rely on frame matching. However, the recognition speed is relatively slow and it can't be used in practice. The LVCSR-based method needs to convert the speech signal into text signal before recognition, which has an
One of the most important steps in a keyword spotting (KWS) system is a post-processing procedure to compute a confidence measure (CM) for each hypothesized keyword. The CM is commonly estimated by likelihood-based acoustic scores. However durations of the detected keyword, which include useful information, has not
challenging research work. This paper proposes a method LET(LDA&Entropy&Tex-trank) to extract topic keywords from Sina Weibo topics text sets. LET considers both topic influence of keywords and topic discrimination of keyword that combines the merits of LDA, Entropy and TextRank. In addition, we design a new standard
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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