Squeeze-and-excitation 3D convolutional attention recurrent network for end-to-end speech emotion recognition

Nasir Saleem; Hela Elmannai; Sami Bourouis; Aymen Trigui

doi:10.1016/j.asoc.2024.111735

ScienceGate Book Chapters

JOURNAL ARTICLE

Squeeze-and-excitation 3D convolutional attention recurrent network for end-to-end speech emotion recognition

Nasir Saleem Hela Elmannai Sami Bourouis Aymen Trigui

Year: 2024 Journal: Applied Soft Computing Vol: 161 Pages: 111735-111735 Publisher: Elsevier BV

DOI: 10.1016/j.asoc.2024.111735

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Spectrogram Speech recognition Robustness (evolution) Feature extraction Convolutional neural network Artificial intelligence Pattern recognition (psychology) Representation (politics) Feature learning Context (archaeology)

Metrics

Cited By

21.93

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Emotion and Mood Recognition

Social Sciences → Psychology → Experimental and Cognitive Psychology

Speech and Audio Processing

Physical Sciences → Computer Science → Signal Processing

Speech Recognition and Synthesis

Physical Sciences → Computer Science → Artificial Intelligence

Squeeze-and-excitation 3D convolutional attention recurrent network for end-to-end speech emotion recognition

Metrics

Citation History

Topics

Related Documents

Adieu Features? End-to-end Speech Emotion Recognition using a Deep Convolutional Recurrent Network

Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network

End-to-End Speech Emotion Recognition Based on One-Dimensional Convolutional Neural Network

End-to-end attention convolutional recurrent network for online handwritten Chinese text recognition

End-to-end speech emotion recognition based on neural network