Deep learning based on the application of voice emotion

Jiang Jinyan

doi:10.54254/2755-2721/13/20230712

Research Article

Open access

Published on 23 October 2023

Download pdf

Jinyan,J. (2023). Deep learning based on the application of voice emotion. Applied and Computational Engineering,13,76-80.

Export citation

Deep learning based on the application of voice emotion

Jiang Jinyan *^,1,

¹ Southwest University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/13/20230712

Abstract

A language is a valuable tool for human development and progress, and it is also an important medium for human beings to transmit information and express emotions. Language signals are ubiquitous, and it is an indispensable part of human life. This article will take the analysis of language as the starting point, combined with the relevant content of computer deep learning, and summarize various methods of language emotion recognition based on a convolutional neural network. In recent years, with the gradual intelligentization of computers, more in-depth discoveries and research have been made on language emotion research. In the deep neural network sector, most of the models used are CNN, LSTM, MO-LSTM models, and this paper aims to propose a new CLDNN (CONVOLUTIONAL, LONG SHORT-TERM) that integrates CNN, DNN, and LSTM into the same network. MEMORY, FULL CONNECTED DEEP NEURAL NETWORKS) model, compare with it, and summarize the advantages and disadvantages of CLDNN.

Keywords

convolutional neural network, CLDNN, emotion recognition, LSTM, voice recognition

View pdf

References

[1]. Facial Liu Runqing. The School of Western Linguistics [M]. Revised Edition. Beijing: Foreign Language Teaching Press, 2013

[2]. Cao Richang, "Tong Psychology" Volume II, p. 44, People's Education Publishing House, 1979 edition

[3]. Yang Lijia Speech Emotion Recognition Algorithm Based on Spectrogram 2021

[4]. Jiang Xiuhao Emotion Recognition Based on Human Movement 2021

[5]. Huang Kun, Zheng Mingxuan, Luo Shichao, Jin Jian, Research on User Emotions and Influencing Factors Based on Facial Expression Recognition in Exploratory Search 10.13266/j.issn.0252-3116.2022.05.010

[6]. Zhang Y D, Yang Z J, Lu H M, et al. Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation[J]. IEEE Access, 2016, 4:8375-8385.

[7]. Comprehensive Application of CNN and LSTM in short-term stock price rise and fall prediction of cyclical stocks 2022.6

[8]. Yan Yanchao Research on emotion recognitionmethod based on continuous wave sound signal 2021.5

[9]. Hou Yixin explains in detail the application of convolutional neural network (CNN) in speech recognition.

Cite this article

Jinyan,J. (2023). Deep learning based on the application of voice emotion. Applied and Computational Engineering,13,76-80.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-017-2(Print) / 978-1-83558-018-9(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.13

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).