Xin Qi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xin Qi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Guowei Chen
Communication University of China
1 shared publication - Pengzhou Zhang
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
1 shared publication - Qing Song
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
1 shared publication - Yao Fu
State Key Laboratory of Media Convergence and Communication, Communication University of China, Dingfuzhuang, Beijing 100024, China
1 shared publication
Latest Publications
- Open Access
- 21 February 2025
- Published by MDPI AG in Electronics
- Vol. 14 (5) , 844
Acoustic Feature Excitation-and-Aggregation Network Based on Multi-Task Learning for Speech Emotion Recognition
In recent years, substantial research has focused on emotion recognition using multi-stream speech representations. In existing multi-stream speech emotion recognition (SER) approaches, effectively extracting and fusing speech features is crucial. To overcome the bottleneck in SER caused by the fusion of inter-feature information, including challenges like modeling complex feature relations and the inefficiency of fusion methods, this paper proposes an SER framework based on multi-task learning, named AFEA-Net. The framework consists of a speech emotion alignment learning (SEAL), an acoustic feature excitation-and-aggregation mechanism (AFEA), and a continuity learning. First, SEAL aligns sentiment information between WavLM and Fbank features. Then, we design an acoustic feature excitation-and-aggregation mechanism to adaptively calibrate and merge the two features. Furthermore, we introduce a continuity learning strategy to explore the distinctiveness and complementarity of dual-stream features from intra- and inter-speech. Experimental results on the publicly available IEMOCAP and RAVDESS sentiment datasets show that our proposed approach outperforms state-of-the-art SER approaches. Specifically, we achieve 75.1% WA, 75.3% UAR, 76% precision, and 75.4% F1-score on IEMOCAP, and 80.3%, 80.6%, 80.8%, and 80.4% on RAVDESS, respectively.
- 21 February 2025
- Published by MDPI AG in Electronics
- Vol. 14 (5) , 844
Open AccessAcoustic Feature Excitation-and-Aggregation Network Based on Multi-Task Learning for Speech Emotion Recognition
In recent years, substantial research has focused on emotion recognition using multi-stream speech representations. In existing multi-stream speech emotion recognition (SER) approaches, effectively extracting and fusing speech features is crucial. To overcome the bottleneck in SER caused by the fusion of inter-feature information, including challenges like modeling complex feature relations and the inefficiency of fusion methods, this paper proposes an SER framework based on multi-task learning, named AFEA-Net. The framework consists of a speech emotion alignment learning (SEAL), an acoustic feature excitation-and-aggregation mechanism (AFEA), and a continuity learning. First, SEAL aligns sentiment information between WavLM and Fbank features. Then, we design an acoustic feature excitation-and-aggregation mechanism to adaptively calibrate and merge the two features. Furthermore, we introduce a continuity learning strategy to explore the distinctiveness and complementarity of dual-stream features from intra- and inter-speech. Experimental results on the publicly available IEMOCAP and RAVDESS sentiment datasets show that our proposed approach outperforms state-of-the-art SER approaches. Specifically, we achieve 75.1% WA, 75.3% UAR, 76% precision, and 75.4% F1-score on IEMOCAP, and 80.3%, 80.6%, 80.8%, and 80.4% on RAVDESS, respectively.