Xiaofen Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaofen Yang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Mingyan Zhang
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Shouyan Chen
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Tao Zou
Guangzhou University
1 shared publication - Xinqi Sun
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
1 shared publication - Zhijia Zhao
School of Mechanical and Electrical Engineering and Guangdong Key Laboratory of Low-altitude Intelligent Unmanned Systems Technology for General Universities, Guangzhou University, Guangzhou, China
1 shared publication
Latest Publications
- Open Access
The Impact of Attention Mechanisms on Speech Emotion Recognition
Speech emotion recognition (SER) plays an important role in real-time applications of human-machine interaction. The Attention Mechanism is widely used to improve the performance of SER. However, the applicable rules of attention mechanism are not deeply discussed. This paper discussed the difference between Global-Attention and Self-Attention and explored their applicable rules to SER classification construction. The experimental results show that the Global-Attention can improve the accuracy of the sequential model, while the Self-Attention can improve the accuracy of the parallel model when conducting the model with the CNN and the LSTM. With this knowledge, a classifier (CNN-LSTM×2+Global-Attention model) for SER is proposed. The experiments result show that it could achieve an accuracy of 85.427% on the EMO-DB dataset.
Open AccessThe Impact of Attention Mechanisms on Speech Emotion Recognition
Speech emotion recognition (SER) plays an important role in real-time applications of human-machine interaction. The Attention Mechanism is widely used to improve the performance of SER. However, the applicable rules of attention mechanism are not deeply discussed. This paper discussed the difference between Global-Attention and Self-Attention and explored their applicable rules to SER classification construction. The experimental results show that the Global-Attention can improve the accuracy of the sequential model, while the Self-Attention can improve the accuracy of the parallel model when conducting the model with the CNN and the LSTM. With this knowledge, a classifier (CNN-LSTM×2+Global-Attention model) for SER is proposed. The experiments result show that it could achieve an accuracy of 85.427% on the EMO-DB dataset.