融合CFCC和Teager能量算子倒谱参数的语音识别

doi:10.11896/j.issn.1002-137X.2019.05.044

摘要/Abstract

摘要： 针对现有表征语音特性的特征提取不完善的问题,提出了一种耳蜗滤波倒谱系数(Cochlear Filter Cepstral Coefficients,CFCC)和Teager能量算子倒谱参数(Teager Energy Operators Cepstral Coefficients,TEOCC)相互融合的方法。该方法将表征人耳听觉特性的CFCC和体现非线性能量特性的TEOCC的融合特征应用到语音识别系统中,并联合主成分分析(Principal Components Analysis,PCA)对该融合特征进行特征选择和优化,最后通过支持向量机进行语音识别。实验结果表明:该融合特征与单一特征相比具有更佳的语音识别性能,结合PCA后其语音识别的准确率平均提高了3.7%。

关键词: Teager能量算子倒谱参数, 耳蜗滤波倒谱系数, 语音识别, 主成分分析

Abstract: In view of the imperfection of the existing features which represent the speech characteristics,this paper proposed a mutual integration method based on Cochlear Filter Cepstral Coefficients and Teager Energy Operators Cepstral Coefficients.First,the fusion feature of CFCC that reflects human auditory characteristics and TEOCC that embodies nonlinear energy characteristics is applied to speech recognition system.Then principal component analysis is applied to the selection and optimization of fusion features.Finally,support vector machine is used for speech recognition.The results show that the proposed fusion features can achieve better speech recognition performance than single feature,and after combining PCA,the accuracy rate of speech recognition is increased by 3.7% on average.

Key words: CFCC, PCA, Speech recognition, TEOCC

中图分类号:

TN912.34

史燕燕, 白静. 融合CFCC和Teager能量算子倒谱参数的语音识别[J]. 计算机科学, 2019, 46(5): 286-289. https://doi.org/10.11896/j.issn.1002-137X.2019.05.044

SHI Yan-yan, BAI Jing. Speech Recognition Combining CFCC and Teager Energy Operators Cepstral Coefficients[J]. Computer Science, 2019, 46(5): 286-289. https://doi.org/10.11896/j.issn.1002-137X.2019.05.044

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