Abstract
We propose a novel method for extracting text feature from the automatic speech recognition (ASR) results in semantic video retrieval. We combine HowNet-rule-based knowledge with statistic information to build special concept lexicons, which can rapidly narrow the vocabulary and improve the retrieval precision. Furthermore, we use the term precision (TP) weighting method to analyze ASR texts. This weighting method is sensitive to the sparse but important terms in the relevant documents. Experiments show that the proposed method is effective for semantic video retrieval.
This work was supported by Beijing Science and Technology Planning Program of China (D0106008040291), the Key Project of Beijing Natural Science Foundation (4051004), and the Key Project of International Science and Technology Cooperation (2005DFA11060).
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Cao, J., Li, J., Zhang, Y., Tang, S. (2006). A Novel Method for Spoken Text Feature Extraction in Semantic Video Retrieval. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_32
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DOI: https://doi.org/10.1007/11922162_32
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