Research Article
Improved Speech Emotion Recognition Using LAM and CTC
@INPROCEEDINGS{10.1007/978-3-030-89814-4_55, author={Lingyuan Meng and Zhe Sun and Yang Liu and Zhen Zhao and Yongwei Li}, title={Improved Speech Emotion Recognition Using LAM and CTC}, proceedings={Mobile Multimedia Communications. 14th EAI International Conference, Mobimedia 2021, Virtual Event, July 23-25, 2021, Proceedings}, proceedings_a={MOBIMEDIA}, year={2021}, month={11}, keywords={Speech emotion recognition Attention CTC VGFCC IEMOCAP}, doi={10.1007/978-3-030-89814-4_55} }- Lingyuan Meng
Zhe Sun
Yang Liu
Zhen Zhao
Yongwei Li
Year: 2021
Improved Speech Emotion Recognition Using LAM and CTC
MOBIMEDIA
Springer
DOI: 10.1007/978-3-030-89814-4_55
Abstract
Time sequence based speech emotion recognition methods are difficult to distinguish between emotional and non-emotional frames of speech, and cannot calculate the amount of emotional information carried by emotional frames. In this paper, we propose a speech emotion recognition method using Local Attention Mechanism (LAM) and Connectionist Temporal Classification (CTC) to deal with these issues. First, we extract the Variational Gammatone Cepstral Coefficients (VGFCC) emotional feature from the speech as the input of LAM-CTC shared encoder. Second, CTC layer performs automatic hard alignment, which allows the network to have the largest activation value at the emotional key frame of the voice. LAM layer learns different degrees on the emotional auxiliary frame. Finally, BP neural network is used to integrate the decoding outputs of CTC layer and LAM layer to obtain emotion prediction results. Evaluation on IEMOCAP shows that the proposed model outperformed the state-of-the-art methods with a UAR of 68.5% and an WAR of 68.1% respectively.
