|Table of Contents|

[1] Zhao Yan, Zhao Li, Lu Cheng, Li Sunan, et al. Multi-head attention-based long short-term memorymodel for speech emotion recognition [J]. Journal of Southeast University (English Edition), 2022, 38 (2): 103-109. [doi:10.3969/j.issn.1003-7985.2022.02.001]
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Multi-head attention-based long short-term memorymodel for speech emotion recognition()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
38
Issue:
2022 2
Page:
103-109
Research Field:
Computer Science and Engineering
Publishing date:
2022-06-20

Info

Title:
Multi-head attention-based long short-term memorymodel for speech emotion recognition
Author(s):
Zhao Yan1;  Zhao Li1;  Lu Cheng1;  Li Sunan1;  Tang Chuangao2;  Lian Hailun1
1School of Information Science and Engineering, Southeast University, Nanjing 210096, China
2School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
Keywords:
speech emotion recognition;  long short-term memory(LSTM);  multi-head attention mechanism;  frame-level features;  self-attention
PACS:
TP37
DOI:
10.3969/j.issn.1003-7985.2022.02.001
Abstract:
To fully make use of information from different representation subspaces, a multi-head attention-based long short-term memory(LSTM)model is proposed in this study for speech emotion recognition(SER). The proposed model uses frame-level features and takes the temporal information of emotion speech as the input of the LSTM layer. Here, a multi-head time-dimension attention(MHTA)layer was employed to linearly project the output of the LSTM layer into different subspaces for the reduced-dimension context vectors. To provide relative vital information from other dimensions, the output of MHTA, the output of feature-dimension attention, and the last time-step output of LSTM were utilized to form multiple context vectors as the input of the fully connected layer. To improve the performance of multiple vectors, feature-dimension attention was employed for the all-time output of the first LSTM layer. The proposed model was evaluated on the eNTERFACE and GEMEP corpora, respectively. The results indicate that the proposed model outperforms LSTM by 14.6% and 10.5% for eNTERFACE and GEMEP, respectively, proving the effectiveness of the proposed model in SER tasks.

References:

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Memo

Memo:
Biographies: Zhao Yan(1993—), male, Ph. D. candidate; Zhao Li(corresponding author), male, doctor, professor, zhaoli@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61571106, 61633013, 61673108, 81871444).
Citation: Zhao Yan, Zhao Li, Lu Cheng, et al. Multi-head attention-based long short-term memory model for speech emotion recognition[J].Journal of Southeast University(English Edition), 2022, 38(2):103-109.DOI:10.3969/j.issn.1003-7985.2022.02.001.
Last Update: 2022-06-20