东南大学学报（英文版）2022，Vol.38Issue(2)：103-109,7.DOI:10.3969/j.issn.1003-7985.2022.02.001
基于多头注意力长短期记忆模型的语音情感识别方法
Multi-head attention-based long short-term memory model for speech emotion recognition
摘要
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.关键词
语音情感识别/长短期记忆/多头注意力机制/帧级别特征/自注意力Key words
speech emotion recognition/long short-term memory(LSTM)/multi-head attention mechanism/frame-level features/self-attention分类
信息技术与安全科学引用本文复制引用
赵焱,赵力,路成,李溯南,唐传高,连海伦..基于多头注意力长短期记忆模型的语音情感识别方法[J].东南大学学报（英文版）,2022,38(2):103-109,7.基金项目
The National Natural Science Foundation of China(No.61571106,61633013,61673108,81871444). （No.61571106,61633013,61673108,81871444）
