Fast decoding for open vocabulary spoken term detection.
NAACL-Short '09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers(2009)
摘要
Information retrieval and spoken-term detection from audio such as broadcast news, telephone conversations, conference calls, and meetings are of great interest to the academic, government, and business communities. Motivated by the requirement for high-quality indexes, this study explores the effect of using both word and sub-word information to find in-vocabulary and OOV query terms. It also explores the trade-off between search accuracy and the speed of audio transcription. We present a novel, vocabulary independent, hybrid LVCSR approach to audio indexing and search and show that using phonetic confusions derived from posterior probabilities estimated by a neural network in the retrieval of OOV queries can help in reducing misses. These methods are evaluated on data sets from the 2006 NIST STD task.
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关键词
audio indexing,audio transcription,OOV query,OOV query term,information retrieval,search accuracy,sub-word information,NIST STD task,broadcast news,business community,open vocabulary,term detection
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