[1]李克粉,王直.改进的小波阈值去噪在语音识别中的应用[J].计算机技术与发展,2013,(05):231-234.
　LI Ke-fen,WANG Zhi.Application of Improved Wavelet Threshold Denoising in Speech Recognition[J].,2013,(05):231-234.
点击复制

改进的小波阈值去噪在语音识别中的应用()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年05期
页码:
231-234
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
Application of Improved Wavelet Threshold Denoising in Speech Recognition
文章编号:
1673-629X（2013）05-0231-04
作者:
李克粉; 王直
江苏科技大学 计算机科学与工程学院
Author(s):
LI Ke-fen; WANG Zhi
关键词:
语音识别; 噪声; 小波变换; 阈值函数
Keywords:
speech recognition; noise; wavelet transform; threshold function
文献标志码:
A
摘要:
目前消除环境噪声提高语音信号质量是语音识别技术研究的重要方向之一.小波阈值去噪法是一种常见的得到广泛应用的语音增强方法,然而传统的小波阈值去噪法存在许多不足,如小波系数经过硬阈值函数处理后在正负阈值处不连续,重构后的信号会产生振荡;经过软阈值函数去噪后的语音过于平滑而丢失语音的突变部分影响语音信号的质量.文中将小波去噪应用于语音识别系统的前端处理中,提出一种新的阈值函数,对小波阈值去噪方法中传统阈值函数的缺点进行改进,将带噪语音信号去噪后再进行识别.实验结果表明,新提出的方法使带背景噪声的语音识别系统的识别性能得到显著的提高
Abstract:
At present,removal of environmental noise to improve the quality of voice signal has become an important direction for voice recognition technology. Wavelet threshold denoising method is a method widely applied to speech enhancement. However,the traditional wavelet threshold denoising method has many deficiencies,like wavelet coefficients processed by the hard threshold function is discontinu-ous in the positive and negative threshold values,resulting in the reconstructed signal oscillation;denoised voice processed by soft thresh-old function is too smooth and lose voice mutation part,which would affect the quality of the voice signal. It applied wavelet denoising to the front-end processing of speech recognition system,and proposed a new threshold function to improve the shortcomings of the tradi-tional threshold function of the wavelet threshold denoising method. It denoised speech signal with noise,and then identified. The experi-mental results show that the new proposed method makes speech recognition performance of the system with background noise significant-ly improved

相似文献/References:

[1]宋鑫坤 陈万米 朱明 桂春胜 程硕远 陈海波.基于正则表达式的语音识别控制策略研究[J].计算机技术与发展,2010,(02):106.
　SONG Xin-kun,CHEN Wan-mi,ZHU Ming,et al.Study on Speech Recognition Control Strategy Based on Regular Expression[J].,2010,(05):106.
[2]张艳 陈仁文.电磁流量计中的抗干扰技术[J].计算机技术与发展,2010,(05):242.
　ZHANG Yan,CHEN Ren-wen.Anti-Interference Technologies in Electromagnetic Flowmeter[J].,2010,(05):242.
[3]黄伟 周鸣争 李小牛.基于滤波器叠加的彩色图像矢量中值滤波方法[J].计算机技术与发展,2008,(01):143.
　HUANG Wei,ZHOU Ming-zheng,LI Xiao-niu.Color Image Vector Median Filtering Method Based on Superimposed Filter[J].,2008,(05):143.
[4]张芳 张亚鸣.基于GA的ANFIS在自适应噪声消除中的应用[J].计算机技术与发展,2007,(05):52.
　ZHANG Fang,ZHANG Ya-ming.Application of Adaptive Noise Cancellation with ANFIS Based on GA[J].,2007,(05):52.
[5]石现峰 张学智 张峰.基于HTK的语音识别系统设计[J].计算机技术与发展,2006,(10):37.
　SHI Xian-feng,ZHANG Xue-zhi,ZHANG Feng.Design of Speech Recognition System Based on HTK[J].,2006,(05):37.
[6]朱宇 宋艳.嵌入式语音识别系统特征参数提取研究[J].计算机技术与发展,2011,(07):246.
　ZHU Yu,SONG Yan.Research of Characteristic Parameters Extraction Based on Embedded Speech Recognition System[J].,2011,(05):246.
[7]林鸣霄.基于SpeechSDK的语音识别技术在三维仿真中的应用[J].计算机技术与发展,2011,(11):160.
　LIN Ming-xiao.Application of Speech Recognition Technology in 3D Simulation Based on Speech SDK[J].,2011,(05):160.
[8]龙钧宇 余红 余爱民.多尺度增强算法在图像边缘检测上的应用研究[J].计算机技术与发展,2012,(12):228.
　LONG Jun-yu,YU Hong,YU Ai-min.Research on Application of Multi-scale Contrast Enhancement Algorithm at Image Edge Detection[J].,2012,(05):228.
[9]王友国,郑克,陆望.一类非线性网络系统中噪声改善信号的相关性[J].计算机技术与发展,2014,24(05):95.
　WANG You-guo,ZHENG Ke,LU Wang.Noise-improved Signal Correlation in a Nonlinear Network System[J].,2014,24(05):95.
[10]王海洋,郭星. 基于语音识别的智慧旅游系统研究[J].计算机技术与发展,2015,25(05):143.
　WANG Hai-yang,GUO Xing. Study on Smart Tourism System Based on Voice Recognition[J].,2015,25(05):143.

更新日期/Last Update: 1900-01-01