%0 Journal Article
%T 基于子空间学习和特征选择融合的语音情感识别<br>Joint subspace learning and feature selection method for speech emotion recognition
%A 宋鹏
%A 郑文明
%A 赵力
%J 清华大学学报（自然科学版）
%D 2018
%R 10.16511/j.cnki.qhdxxb.2018.26.014
%X 传统语音情感识别主要基于单一情感数据库进行训练与测试。而实际情况中，训练语句和测试语句往往来源于不同的数据库，识别率较低。为此，该文提出一种基于子空间学习和特征选择融合的语音情感识别方法。通过采用回归方法来学习特征的子空间表示；同时，引入l2，1-范数用于特征的选择和最大均值差异（maximum mean discrepancy，MMD）来减少不同情感数据库间的特征差异，进行联合优化求解从而提取较为鲁棒的情感特征表示。在EMO-DB和eNTERFACE这2个公开情感数据库上进行实验评价，结果表明：该方法在跨库条件下具有较好的性能，比其他经典的迁移学习方法更加鲁棒高效。<br>Abstract：Traditional speech emotion recognition methods are trained and evaluated on a single corpus. However, when the training and testing use different corpora, the recognition performance drops drastically. A joint subspace learning and feature selection method is presented here to imprive recognition. In this method, the feature subspace is learned via a regression algorithm with the l2,1-norm used for feature selection. The maximum mean discrepancy (MMD) is then used to measure the feature divergence between different corpora. Tests show this algorithm gives satisfactory results for cross-corpus speech emotion recognition and is more robust and efficient than state-of-the-art transfer learning methods.
%K 特征选择
%K 子空间学习
%K 情感识别
%K &lt
%K br&gt
%K feature selection
%K subspace learning
%K emotion recognition
%U http://jst.tsinghuajournals.com/CN/Y2018/V58/I4/347