A high-performance Cantonese keyword search system
Acoustics, Speech and Signal Processing(2013)
摘要
We present a system for keyword search on Cantonese conversational telephony audio, collected for the IARPA Babel program, that achieves good performance by combining postings lists produced by diverse speech recognition systems from three different research groups. We describe the keyword search task, the data on which the work was done, four different speech recognition systems, and our approach to system combination for keyword search. We show that the combination of four systems outperforms the best single system by 7%, achieving an actual term-weighted value of 0.517.
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关键词
natural language processing,speech recognition,telephony,Cantonese conversational telephony audio,Cantonese keyword search system,IARPA Babel program,diverse speech recognition,term-weighted value,deep learning,keyword search,spoken term detection,system combination
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