Speech Emotion Recognition Using Local and Global Features

Yuanbo Gao, Baobin Li, Ning Wang, Tingshao Zhu

Abstract: Speech is an easy and useful way to detect speakers’ mental and psychological health, and automatic emotion recognition in speech has been investigated widely in the fields of human-machine interaction, psychology, psychiatry, etc. In this paper, we extract prosodic and spectral features including pitch, MFCC, intensity, ZCR and LSP to establish the emotion recognition model with SVM classifier. In particular, we find different frame duration and overlap have different influences on final results. So, Depth-First-Search method is applied to find the best parameters. Experimental results on two known databases, EMODB and RAVDESS, show that this model works well, and our speech features are enough effectively in characterizing and recognizing emotions.

External IDs:dblp:conf/brain/GaoLWZ17