Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (5): 213-215.

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Application of support vector machines in low SNR speech recognition

GUO Chao1, ZHANG Xueying1, LIU Xiaofeng2   

  1. 1.College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    2.Department of Math, College of Science, Taiyuan University of Technology, Taiyuan 030024, China
  • Online:2013-03-01 Published:2013-03-14

支持向量机在低信噪比语音识别中的应用

郭  超1,张雪英1,刘晓峰2   

  1. 1.太原理工大学 信息工程学院,太原 030024
    2.太原理工大学 理学院 数学系,太原 030024

Abstract: A low SNR speech recognition system for isolated words and non-specific speakers is constructed in this paper. Improved MFCC speech features (Mel-Frequency Discrete Wavelet Cepstral Coefficients, MFDWCs) are adopted and Support Vector Machines(SVM) is utilized as classification algorithm. The system obtains higher recognition accuracy, comparing to the results based on RBF Artificial Neural Network(ANN). The experimental results show SVM possesses better robustness than RBF ANN, especially in low SNRs.

Key words: support vector machines, Gaussian kernel, speech recognition, low Signal Noise Ratio(SNR)

摘要: 采用改进的MFCC语音特征参数(Mel频率离散小波倒谱系数),使用支持向量机作为分类算法,构建了低信噪比环境下的孤立词非特定人语音识别系统,取得了较高的识别率。将实验结果与基于RBF神经网络的识别结果进行比较,结果表明在低信噪比时,SVM的识别率比使用RBF神经网络有较大提高,具有非常好的鲁棒性。

关键词: 支持向量机, Gaussian核, 语音识别, 低信噪比