scholarly journals Speech Emotion Recognition Using Convolutional- Recurrent Neural Networks with Attention Model

2017 ◽  
Author(s):  
YAWEI MU ◽  
LUIS A. HERNÁNDEZ GÓMEZ ◽  
ANTONIO CANO MONTES ◽  
CARLOS ALCARAZ MARTÍNEZ ◽  
XUETIAN WANG ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Speech Emotion Recognition ◽  
Attention Model

3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition

2018 ◽  
Vol 25 (10) ◽  
pp. 1440-1444 ◽  
Author(s):  
Mingyi Chen ◽  
Xuanji He ◽  
Jing Yang ◽  
Han Zhang
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Speech Emotion Recognition ◽  
Attention Model

Convolutional Recurrent Neural Networks Based Speech Emotion Recognition

2020 ◽  
Vol 17 (8) ◽  
pp. 3786-3789
Author(s):  
P. Gayathri ◽  
P. Gowri Priya ◽  
L. Sravani ◽  
Sandra Johnson ◽  
Visanth Sampath
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Machine Learning Techniques ◽  
Speech Emotion Recognition ◽  
Emotional Information ◽  
Feature Representations ◽  
Emotional Factors ◽  
Learning Techniques ◽  
The Impact

Recognition of emotions is the aspect of speech recognition that is gaining more attention and the need for it is growing enormously. Although there are methods to identify emotion using machine learning techniques, we assume in this paper that calculating deltas and delta-deltas for customized features not only preserves effective emotional information, but also that the impact of irrelevant emotional factors, leading to a reduction in misclassification. Furthermore, Speech Emotion Recognition (SER) often suffers from the silent frames and irrelevant emotional frames. Meanwhile, the process of attention has demonstrated exceptional performance in learning related feature representations for specific tasks. Inspired by this, propose a Convolutionary Recurrent Neural Networks (ACRNN) based on Attention to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas is used as input. Finally, experimental results show the feasibility of the proposed method and attain state-of-the-art performance in terms of unweighted average recall.

Speech emotion recognition using recurrent neural networks with directional self-attention

2021 ◽  
Vol 173 ◽  
pp. 114683
Author(s):  
Dongdong Li ◽  
Jinlin Liu ◽  
Zhuo Yang ◽  
Linyu Sun ◽  
Zhe Wang
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Speech Emotion Recognition

Speech Emotion Recognition using Convolutional Recurrent Neural Networks and Spectrograms

2020 ◽  
Author(s):  
Mustafa A. Qamhan ◽  
Ali H. Meftah ◽  
Sid-Ahmed Selouani ◽  
Yousef A. Alotaibi ◽  
Mohammed Zakariah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Speech Emotion Recognition
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