计算机工程与应用Issue(10)：219-222,226,5.DOI:10.3778/j.issn.1002-8331.1309-0381
采用GW-MFCC模型空间参数的语音情感识别
Speech emotion recognition using GW-MFCC feature
摘要
Abstract
Aiming the insufficient expression of speech emotion with single type of speech features, a new feature weight-ed MFCC(WMFCC) is proposed combining LSF with good interpolation and quantization performance and MFCC which presents human hearing characters. GMM model is applied to this feature to obtain high level model space parameter GW-MFCC in order to further improve the emotion recognition rate with detailed information. Experiments are carried out on EMO-DB. The correct recognition rates are 5.7% and 6.9% higher than using MFCC and LSF respectively. The experiment results show that the GW-MFCC feature can effectively convey emotional information in speech, thus can improve the performance in the emotion recognition.关键词
语音情感识别/线谱对频率(LSF)/Mel频率倒谱系数(MFCC)/高斯混合模型/模型空间Key words
speech emotion recognition/Linear Spectrum Frequence(LSF)/Mel-Frequency Cepstral Coeffients(MFCC)/Gaussian Mixture Model(GMM)/model space分类
信息技术与安全科学引用本文复制引用
沈燕,肖仲喆,李冰洁,周孝进,周强,陶智..采用GW-MFCC模型空间参数的语音情感识别[J].计算机工程与应用,2015,(10):219-222,226,5.基金项目
江苏省高校自然科学研究（No.12KJB510027）。 （）
