[1]ZHANG Lei,CHEN Jing,XIANG Xue-zhi,et al.Research of keyword spotting based on a keyword spotting confusion network[J].CAAI transactions on intelligent systems,2010,5(5):432-435.[doi:10.3969/j.issn.1673-4785.2010.05.009]
Copy

Research of keyword spotting based on a keyword spotting confusion network

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 5 Page number: 432-435 Column: 学术论文—自然语言处理与理解 Public date: 2010-10-25
Title:
Research of keyword spotting based on a keyword spotting confusion network
Author(s):
ZHANG Lei;  CHEN Jing;  XIANG Xue-zhi;  JIA Mei-mei
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
keyword spotting;  confusion network;  speech recognition
CLC:
TP391；TN912
DOI:
10.3969/j.issn.1673-4785.2010.05.009
Abstract:
In order to achieve a higher keyword recall rate from large vocabulary continuous speech recognition (LVCSR) and minimize the word error rate, a confusion network was used in a keyword spotting system. Moreover, an improved method of generating a keyword confusion network which was more suitable for keyword spotting was proposed based on the traditional algorithm. This method only focused on keyword competitions, and was capable of transforming all the keyword competitions into a confusion network with a marked score, and highlighted competitions to all the candidates. Compared with the traditional keyword spotting system which uses N-best as the medium structure, the proposed method increased the recall rate of confusion network to 87.11%; compared with the keyword spotting system based on N-best, there is a 21.65% improvement in the recall rate. Experiments show the proposed method could locate keywords directly, besides increasing the recall rate, so the system costs less time.
References:
［1］叶靓，王智斌，邵谦明.基于相关反馈的语音检索引擎［J］.计算机工程, 2007, 33(17): 228-230.
YE Liang, WANG Zhibin, SHAO Qianming. Speech retrieval engine based on relevance feedback［J］. Computer Engineering, 2007, 33(17): 228-230.
［2］王让定，袁旭海，徐霁.一种新颖的混合语音检索算法［J］.计算机应用研究, 2008, 25(5): 1349-1351.
WANG Rangding, YUAN Xuhai, XU Ji. Novel mixing speech retrieval algorithm［J］. Application Research of Computers, 2008, 25(5): 1349-1351.
［3］陈立伟，宋宪晨，章东华，等.一种基于优化神经网络的语音识别［J］.应用科技， 2008， 35（2）： 17-20.
?CHEN Liwei,SONG Xianchen,ZHANG Dongsheng,et al.Speech recognition using an optimized wavelet neural network［J］. Applied Science and Technology, 2008, 35(2)：17-20.
［4］郑铁然，韩纪庆. 汉语语音检索中基于音节的索引方法研究［C］//第八届全国人机语音通讯学术会议论文集. 北京, 中国, 2005: 419-424. 
ZHENG Tieran, HAN Jiqing. Study on syllable based indexing methods in mandarin speech retrieval［C］//Proceedings of National Conference on Man-Machine Speech Communication. Beijing, China, 2005: 419-424.
［5］MANGU L, BRILL E, STOLCKE A. Finding consensus in speech recognition: word error minimization and other applications of confusion networks［J］. Computer Speech and Language, 2000, 14(4): 373-400.
［6］XUE Jian, ZHAO Yunxin. Improved confusion network algorithm and shortest path search from word Lattice［C］//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, USA, 2005: 853-856.
［7］ZHANG Pengyuan, SHAO Jian, ZHAO Qingwei, et al. Keyword spotting based on syllable confusion network ［C］//The Third International Conference on Natural Computing. Haikou, China, 2007: 656-659.
［8］YONG S, EVERMANN G, GALES M. The HTK book(for HTK 3.3)［EB/OL］. ［2009-11-25］. Http: //htk.eng.cam.ac.uk.
［9］GOODMAN J T. A bit of progress in language modeling［J］. Computer Speech and Language, 2001, 15(4): 403-434.
Similar References:

Memo

-

Last Update: 2010-11-26

Copyright © CAAI Transactions on Intelligent Systems