Graph Learning Based Speaker Independent Speech Emotion Recognition

Advances in Electrical and Computer Engineering

XU, X. ; HUANG, C. ; WU, C. ; WANG, Q. ; ZHAO, L..

关键词: speech emotion recognition;    speaker penalty graph learning;    graph embedding framework;    dimensionality reduction;   

DOI  :  10.4316/AECE.2014.02003

学科分类：计算机科学（综合）

来源: Universitatea "Stefan cel Mare" din Suceava

【 摘 要 】

In this paper, the algorithm based on graph learning and graph embedding framework, Speaker-Penalty Graph Learning (SPGL), is proposed in the research of speech emotion recognition to solve the problems caused by different speakers. Graph embedding framework theory is used to construct the dimensionality reduction stage of speech emotion recognition. Special penalty and intrinsic graphs of the graph embedding framework is proposed to penalize the impacts from different speakers in the task of speech emotion recognition. The original speech emotion features are extracted by various categories, reflecting different characteristics of each speech sample. According to the experiments in speech emotion corpus using different classifiers, the proposed method with linear and kernelized mapping forms can both achieve relatively better performance than the state-of-the-art dimensionality reduction methods.

【 授权许可】

Unknown