%0 Journal Article
%T Research on multiple-classifiers fusion for speech recognition<br>基于多分类器融合的语音识别方法研究
%A KONG Hao
%A YANG Yong
%A WANG Guo-yin
%A <br>孔浩
%A 杨勇
%A 王国胤
%J 重庆邮电大学学报(自然科学版)
%D 2011
%I 
%X Fusion of multiple classifiers can be integrated superiority of the each classifier, and better recognition effect can be achieved. At present, the multi-classifier fusion is a hot topic in pattern recognition. In a speech recognition system, design of the classifier is the key point to the superiority. In this paper, a novel speech recognition approach based on multi-classifier fusion is proposed. SVM, RBF network and bayes net are fused by weighted voting strategy, and the weight is calculated according to the validation sets from sample database. The experiment results show that the performance of multiple-classifiers fusion is better than single classifier, and the method proposed in this paper is effective.
%K multiple classifier fusion
%K weight vote
%K speech recognition
%K support vector machine(SVM)<br>多分类器融合
%K 加权投票
%K 语音识别
%K 支持向量机（SVM）
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=5C2694A2E5629ECD6B59D7B28C6937AD&aid=8A9892E12714AC7F09A6868B2CDDFC35&yid=9377ED8094509821&vid=EA389574707BDED3&iid=E158A972A605785F&sid=4A2356A1257A12EB&eid=DDDA4F26E8AD3C0E&journal_id=1673-825X&journal_name=重庆邮电大学学报(自然科学版)&referenced_num=0&reference_num=8