Abstract
In this paper, we present a baseline spoken term detection (STD) system for Mongolian speech data. Mongolian speech data is recognized as n-best word lattices by the Mongolian ASR system, and n-best word lattices are translated into word confusion networks. Secondly, individual arcs in the word confusion networks are indexed into an efficient inverted index structure. Finally, we search on this index structure to extract keyword candidates, and re-rank the results. Experiments show that this system can get preferable results for the Mongolian in-vocabulary (IV) query terms detection.
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Bao, F., Gao, G., Bao, Y., Su, X. (2012). The Research on Mongolian Spoken Term Detection Based on Confusion Network. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_74
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DOI: https://doi.org/10.1007/978-3-642-33506-8_74
Publisher Name: Springer, Berlin, Heidelberg
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