QQ客服 官方微博 反馈留言

	基于DDBHMM的LVCSR系统的单步搜索算法
	孙健 ; 王作英 ; SUN Jian ; WANG Zuoying
	2010-06-09 ; 2010-06-09
关键词	大词汇量连续语音识别 单步搜索 段长分布 最大似然状态序列 large vocabulary continuous speech recognition one-stage search algorithm duration distribution-based HMM maximum likelihood states sequence TN912.3
其他题名	One-stage search algorithm for large vocabulary continuous speech recognition based on DDBHMM
中文摘要	为了在大词汇量连续语音识别(LVCSR)系统中能够利用段长信息,该文按树状组织发音词典,利用语言模型预测技术,基于最大似然状态序列(M LSS)算法,给出了采用基于段长分布的隐含M arkov模型(DDBHMM)的LVCSR系统的二元文法语言模型的单步搜索算法。实验结果表明,尽管单步搜索的替代错误率高于双步搜索,但单步搜索的插入和删除错误率都比双步搜索要低,总体性能上单步搜索要好于双步搜索。同时,DDBHMM能较准确地利用了语音信号中的状态段长信息,采用DDBHMM的LVCSR系统比采用经典的齐次HMM的系统有更好的识别性能。; In order to use duration information in a large vocabulary continuous speech recognition(LVCSR) system,the pronunciation dictionary is organized as a tree and the language model look-ahead technique is adopted.Based on the maximum likelihood states sequence algorithm,the one-stage search algorithm for the LVCSR using the duration distribution-based hidden Markov model(DDBHMM) in proposed when the Bigram language model is used.Tests show that,although the two-stage search algorithm has a lower substitute error rate than the single stage one,the insertion errors and deletion errors are both higher than that of the single-stage search.The one-stage search algorithm is,therefore,better than the two-stage search in terms of overall performance.Since the DDBHMM accurately describes the state duration of the speech signals,the DDBHMM system has better performance than system using homogeneous HMM.; 国家“八六三”高技术项目(2001AA114071)
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/54849]
专题	清华大学
推荐引用方式 GB/T 7714	孙健,王作英,SUN Jian,等. 基于DDBHMM的LVCSR系统的单步搜索算法[J],2010, 2010.
APA	孙健,王作英,SUN Jian,&WANG Zuoying.(2010).基于DDBHMM的LVCSR系统的单步搜索算法..
MLA	孙健,et al."基于DDBHMM的LVCSR系统的单步搜索算法".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。