计算机工程与应用2013，Vol.49Issue(5)：213-215,3.DOI:10.3778/j.issn.1002-8331.1107-0460
支持向量机在低信噪比语音识别中的应用
Application of support vector machines in low SNR speech recognition
摘要
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.关键词
支持向量机/Gaussian核/语音识别/低信噪比Key words
support vector machines/Gaussian kernel/speech recognition/low Signal Noise Ratio (SNR)分类
信息技术与安全科学引用本文复制引用
郭超,张雪英,刘晓峰..支持向量机在低信噪比语音识别中的应用[J].计算机工程与应用,2013,49(5):213-215,3.基金项目
国家自然科学基金(No.61072087). （No.61072087）
