智能语音识别系统中噪声估计算法的研究和改进

吴楠; 冯祖勇; 韦高梧

doi:10.12052/gdutxb.170173

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

智能语音识别系统中噪声估计算法的研究和改进

综合研究 | 更新时间：2026-05-27

- 智能语音识别系统中噪声估计算法的研究和改进
- Research and Improvement of Noise Estimation Algorithm in Intelligent Speech Recognition System
- 广东工业大学学报 2018年35卷第3期页码：43-46
- 作者机构：
  
  广东工业大学　物理与光电工程学院,广东,广州,510006
- 作者简介：
  
  吴楠(1992–),男，硕士研究生，主要研究方向为智能通信
  冯祖勇(1975–),男，教授，主要研究方向为能源材料和智能控制. E-mail：fengzuyong@foxmail.com
- 基金信息：
  
  广东省科技计划项目(2016A010104019);广州市科技计划项目(201510010285)
- DOI：10.12052/gdutxb.170173
  中图分类号： TP391.42
- 网络首发：2018-05-09，
  
  纸质出版：2018
- 稿件说明：
移动端阅览
吴楠, 冯祖勇, 韦高梧. 智能语音识别系统中噪声估计算法的研究和改进[J]. 广东工业大学学报, 2018, 35(3): 43-46.

Wu Nan, Feng Zu-yong, Wei Gao-wu. Research and Improvement of Noise Estimation Algorithm in Intelligent Speech Recognition System[J]. 2018, 35(3): 43-46.
吴楠, 冯祖勇, 韦高梧. 智能语音识别系统中噪声估计算法的研究和改进[J]. 广东工业大学学报, 2018, 35(3): 43-46. DOI： 10.12052/gdutxb.170173.

Wu Nan, Feng Zu-yong, Wei Gao-wu. Research and Improvement of Noise Estimation Algorithm in Intelligent Speech Recognition System[J]. 2018, 35(3): 43-46. DOI： 10.12052/gdutxb.170173.

摘要

智能语音识别技术的研究已有较长的时间

但由于语音信号本身所具有的多变性、瞬时性、连续性和动态性的特征

使得机器在不同的环境尤其是噪声环境中进行语音信号的识别仍具有一定的困难. 为了提高带噪语音信号识别的准确率

本文研究了一种常用的噪声估计算法

即基于后验信噪比的时间递归平均算法. 并在此算法的基础上提出了一种对平滑因子的改进算法

将语音活性检测算法与这两种算法在不同输入信噪比下进行模拟验证. 通过运算结果的对比分析可以看出

改进后的算法相比于语音活性检测算法最高可以使输出分段SNR提高2.1 dB

相比于原时间递归平均算法最高可以使输出分段SNR提高0.5 dB

表明低输入SNR下改进后的算法可以有效提高语音信号的质量和可懂度.

Abstract

The research of intelligent speech recognition technology has been going on for a long time. However

due to the characteristics of variability

instantness

continuity and dynamic of the speech signal itself

the identification of the speech still has some difficulties when the machine is put in different environments

especially in the noisy environment. In order to improve the recognition accuracy of the noisy speech signal

a commonly used noise estimation algorithm was studied

which was based on the time-averaged algorithm of posterior signal noise ratio. And an improved algorithm of the smoothing factor was brought up on the basis of the previous algorithm. The voice activity detection algorithm and the above two algorithms were simulated under different input signal-noise ratios. The comparative analysis of the operation results shows that the improved algorithm can improve the output segment SNR by 2.1 dB compared with the voice activity detection algorithm

and it can also improve the output segment SNR by 0.5 dB compared with the original time recursive average algorithm. It is indicated that the improved algorithm can effectively improve the quality and intelligibility of the speech signal at low input SNR.

关键词

Keywords

references

ZHANG Y G, YU Y P. The design of the isolated speech recognition system based on ARM [J]. Journal of Guangdong University of Technology, 2013, 30(2): 95-98.

LIU Y R. Research on speech recognition technology [J]. China Science Technology Panorama Magazine, 2016, 24: 26-27.

LIU J G, ZHOU Yi, MA Y B, et al . Estimation algorithm of switching speech power spectrum for automatic speech recognition system [J ] . Journal of Computer Applications, 2016, 36(12): 3369-3373.

WANG H B, ZHANG J W, TAO L. Effects of noise spectrum estimation algorithms on speech intelligibility [J]. Technical Acoustics, 2015, 34(5): 424-430.

XU Z H, ZHANG T F. Design of smart home system based on speech recognition and wireless sensor network [J]. Computer Measurement Control, 2012, 20(1): 180-182.

祁琳娜. 语音增强改进算法研究及其DSP的实现[D]. 西安: 长安大学信息学院, 2016.

YANG L, LOIZOU P C. Speech enhancement by combining statistical estimators of speech and noise[C]//IEEE International Conference on Acoustics Speech and Signal Processing. Dallas: IEEE, 2010: 4754-4757.

ZHENG Y M, BAO H, ZHANG J. A new speech denoising method based on Wiener Filtering and Wavelet analysis [J]. Journal of Guangdong University of Technology, 2017, 34(5): 52-55.

LU Y, LOIZOU P C. A geometric approach to spectral subtraction [J]. Speech Communication, 2008, 50: 453-466.

CHENG N, LIU W J. A subspace speech enhancement algorithm based on Gaussian-Laplacian-Gamma statistical models and masking properties of human ears [J]. Acta Acustica, 2009, 34(6): 554-565.

LU Y, LOIZOU P C. Estimators of the magnitude squared spectrum and methods for incorporating SNR uncertainty [J]. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(5): 1123-1137.

OU S F, ZHAO X H. Modified priori-SNR estimation for noisy speech enhancement [J]. Journal of Jilin University (Engineering and Technology Edition), 2009, 39(3): 787-791.

EN D, CHEN Y K, MAO Z L. Discontinuous transmission of voice in L-PLC under low SNR based on FastICA [J]. Computer Engineering and Applications, 2016, 52(9): 108-111.

LIN L, HOLMES W H, AMBIKAIRAJAH E. Subband noise estimation for speech enhancement using a perceptual Wiener filter[J]. 2003 IEEE International Conference on Acoustics, 2003, 1(1): 80-83.

COHEN I. Noise spectrum estimation in adverse environments: improved minimal controlled recursive averaging [J]. IEEE Transactions on Speech and Audio Processing, 2003, 11(5): 466-475.

WANG P, ZENG Y M. Speech enhancement approach based on minimal controlled recursive averaging algorithm using bidirectional searching method [J]. Acta Acustica, 2010, 35(1): 81-87.

LIN L, HOLMES W H, AMBIKAIRAJAH E. Adaptive noise estimation algorithm for speech enhancement [J]. Electronics Letters, 2003, 39(9): 754-755.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于智能家居控制的嵌入式语音识别系统

基于ARM的孤立语音识别系统的研究

基于AAGV-YOLOX模型的输电线路异物检测研究

基于数字混频的脉冲式激光测距方法的研究与实现

储能变流器电能质量治理策略研究