Dilated Residual Network with Multi-head Self-attention for Speech Emotion Recognition
Abstract
Speech emotion recognition (SER) plays an important role in intelligent speech interaction. One vital challenge in SER is to extract emotion-relevant features from speech signals. In state-of-the-art SER techniques, deep learning methods, e.g, Convolutional Neural Networks (CNNs), are widely employed for feature learning and have achieved significant performance. However, in the CNN-oriented methods, two performance limitations have raised: 1) the loss of temporal structure of speech in the progressive resolution reduction; 2) the ignoring of relative dependencies between elements in suprasegmental feature sequence. In this paper, we proposed the combining use of Dilated Residual Network (DRN) and Multi-head Self-attention to alleviate the above limitations. By employing DRN, the network can retain high resolution of temporal structure in feature learning, with similar size of receptive field to CNN based approach. By employing Multi-head Self-attention, the network can model the inner dependencies between elements with different positions in the learned suprasegmental feature sequence, which enhances the importing of emotion-salient information. Experiments on emotional benchmarking dataset IEMOCAP have demonstrated the effectiveness of the proposed framework, with 11.7% to 18.6% relative improvement to state-of-the-art approaches.
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The proposed model outperforms state-of-the-art unimodal SER models by at least 6.8% in mean weighted accuracy and 5.9% in mean unweighted accuracy (abstract: about 6% improvement).
“It also outperforms state-of-the-art unimodal SER models [18, 19, 6] by at least 6.8% in mean weighted accuracy and 5.9% in mean unweighted accuracy.”
From Speech Emotion Recognition with Dual-Sequence LSTM Architecture (2020) · cites this paper as [6]
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Wang, B., Liang, J., Ye, C., Yan, T., Liu, M., & Yan, T. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3624242
Adaptive Dzip Transformer-Based Data Compression Technology for Digital Twin Systems
Pan, C., Zhang, C. Y., Shi, J., & Zhao, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3635831
Intermediate-Selective Feature Enhancement for Speech Emotion Recognition
Li, Y., Xing, X., Mai, J., Xing, J., & Xu, X. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434758
Ali, M., Mushtaq, M. F., Akram, U., Hassan, D. S. M., Samee, N. M. A., Rehman, H. M. R. U., & Ashraf, I. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01266-3
- Authors
- Li, R., Wu, Z., Jia, J., Zhao, S., & Meng, H.
- Affiliations
- Tsinghua University (CN)
- Microsoft (United States) (US)
- Chinese University of Hong Kong (HK)
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 6675-6679
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2940259008
- Cited by
- 82
- References
- 22
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 15 · 2023: 8 · 2022: 13 · 2021: 18 · 2020: 11 · 2019: 4
- FWCI
- 14.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speech emotion recognition, dilated residual network, multi-head self-attention, prosodic features, receptive fields, IEMOCAP
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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