摘要
：针对各种数字信息，实现了一种基于ＴＭＳ３２０Ｃ５ｘ 评价模块（ＥＶＭ）的与特定人无关的连接数字语音识别系统．在分析了连续概率密度的隐马尔可夫模型（ＣＤＨＭＭ）基础上，利用ＬＰＣ倒谱系数、ＬＰＣ差分倒谱系数、能量归一化系数及其差分系数作为语音特征矢量，训练和识别采用Ｖｉｔｅｒｂｉ算法和Ｂａｕｍ －Ｗｅｌｃｈ 重估算法，有效地提高了系统的识别率．给出了实现各个阶段所需的时间，比较了简单模板匹配法和隐马尔可夫模型法以及不同语音特征参数对识别率的影响．在具体实现中，着重处理了抗噪及实时实现问题．实验结果表明，本系统在普通机房条件下取得较满意的效果，正确识别率达到９２％ ，为其实用化提供了较为重要的技术途径．
A speaker independent speech recognition system of connected digits was developed based on TMS320C5x Evaluation Module (EVM). Cepstrum coefficients, derivative coefficients of cepstrum, log energy normalization coefficient and derivative coefficient of log energy were used as feature vector of speech recognition on the basis of analyzing continuous density hidden Markov model(CDHMM). The system uses Vterbi and Baum Welch reestimation algorithms as training and recognition algorithms, which improve the recognition accuracy greatly. In the paper, clock cycles required by different phases were given, recognition accuracy was firstly compared between template matching and HMM methods and then compared according to different speech characteristic parameters. In specific implementation, resistant noises and real time performance were considered emphatically. Experimental results show that the recognition rate of this system is about 92%, which provides an important method for its practical purposes.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
1999年第12期1525-1528,共4页
Journal of Shanghai Jiaotong University
关键词
数字识别
隐马氏模型
DSP
语音识别系统
hidden Markov model
speaker recognition
digital recognition
evaluation module
cepstrum coefficients
derivative coefficients of cepstrum
