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
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
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.