Speech Emotion Recognition Based on CNN+LSTM Model
Wei Mou, Pei-Hsuan Shen, Chu-Yun Chu, Yu-Cheng Chiu, Tsung-Hsien Yang, Ming-Hsiang Su
Abstract
Due to the popularity of intelligent dialogue assistant services, speech emotion recognition has become more and more important. In the communication between humans and machines, emotion recognition and emotion analysis can enhance the interaction between machines and humans. This study uses the CNN+LSTM model to implement speech emotion recognition (SER) processing and prediction. From the experimental results, it is known that using the CNN+LSTM model achieves better performance than using the traditional NN model.- Anthology ID:
- 2021.rocling-1.6
- Volume:
- Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
- Month:
- October
- Year:
- 2021
- Address:
- Taoyuan, Taiwan
- Editors:
- Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
- Venue:
- ROCLING
- SIG:
- Publisher:
- The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
- Note:
- Pages:
- 43–47
- Language:
- URL:
- https://aclanthology.org/2021.rocling-1.6
- DOI:
- Cite (ACL):
- Wei Mou, Pei-Hsuan Shen, Chu-Yun Chu, Yu-Cheng Chiu, Tsung-Hsien Yang, and Ming-Hsiang Su. 2021. Speech Emotion Recognition Based on CNN+LSTM Model. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 43–47, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
- Cite (Informal):
- Speech Emotion Recognition Based on CNN+LSTM Model (Mou et al., ROCLING 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.rocling-1.6.pdf