东南大学学报（英文版）Issue(1)：7-12,6.DOI:10.3969/j.issn.1003-7985.2014.01.002
基于半监督判别分析的语音情感识别
Speech emotion recognition using semi-supervised discriminant analysis
摘要
Abstract
Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.关键词
语音情感识别/语音情感特征/半监督判别分析/维数约简Key words
speech emotion/recognition/speech emotion feature/semi-supervised discriminant analysis/dimensionality reduction分类
信息技术与安全科学引用本文复制引用
徐新洲,黄程韦,金赟,吴尘,赵力..基于半监督判别分析的语音情感识别[J].东南大学学报（英文版）,2014,(1):7-12,6.基金项目
The National Natural Science Foundation of China No．6123100261273266 the Ph．D．Programs Foundation of Min-istry of Education of China No．20110092130004． （）
