A Review on Speech Emotion Recognition Using Deep Learning and Attention Mechanism

被引：107

作者：

Lieskovska, Eva ^{[1
]}

Jakubec, Maros ^{[1
]}

Jarina, Roman ^{[1
]}

Chmulik, Michal ^{[1
]}

机构：

[1] Univ Zilina, Fac Elect Engn & Informat Technol, Univ 8215-1, Zilina 01026, Slovakia

来源：

ELECTRONICS | 2021年 / 10卷 / 10期

关键词：

speech emotion recognition; deep learning; attention mechanism; recurrent neural network; long short-term memory; DATA AUGMENTATION; NEURAL-NETWORKS; FEATURES; AUDIO; CLASSIFIERS; PARAMETERS; DOMINANCE; DATABASES; AROUSAL; MODEL;

D O I：

10.3390/electronics10101163

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Emotions are an integral part of human interactions and are significant factors in determining user satisfaction or customer opinion. speech emotion recognition (SER) modules also play an important role in the development of human-computer interaction (HCI) applications. A tremendous number of SER systems have been developed over the last decades. Attention-based deep neural networks (DNNs) have been shown as suitable tools for mining information that is unevenly time distributed in multimedia content. The attention mechanism has been recently incorporated in DNN architectures to emphasise also emotional salient information. This paper provides a review of the recent development in SER and also examines the impact of various attention mechanisms on SER performance. Overall comparison of the system accuracies is performed on a widely used IEMOCAP benchmark database.

引用

页数：29

共 112 条

[71] Modeling the Temporal Evolution of Acoustic Parameters for Speech Emotion Recognition [J].