A Keyword-Aware Language Modeling Approach to Spoken Keyword Search

A keyword-sensitive language modeling framework for spoken keyword search (KWS) is proposed to combine the advantages of conventional keyword-filler based and large vocabulary continuous speech recognition (LVCSR) based KWS systems. The proposed framework allows keyword search systems to be flexible on keyword target settings as in the LVCSR-based keyword search. In low-resource scenarios it facilitates KWS with an ability to achieve high keyword detection accuracy as in the keyword-filler based systems and to attain a low false alarm rate inherent in the LVCSR-based systems. The proposed keyword-aware grammar is realized by incorporating keyword information to re-train and modify the language models used in LVCSR-based KWS. Experimental results, on the evalpart1 data of the IARPA Babel OpenKWS13 Vietnamese tasks, indicate that the proposed approach achieves a relative improvement, over the conventional LVCSR-based KWS systems, of the actual term weighted value for about 50% (from 0.2...

• Type: Journal
• Year: 2016
• Where: VLSISP
• Authors: I-Fan Chen, Chongjia Ni, Boon Pang Lim, Nancy F. Chen, Chin-Hui Lee
• Keywords: VLSI, VLSISP 2016