Enhanced Speech Emotion Recognition Using DCGAN-Based Data Augmentation-Reference-Cited by-同舟云学术

Enhanced Speech Emotion Recognition Using DCGAN-Based Data Augmentation

Published:2023-09-20 Issue:18 Volume:12 Page:3966
ISSN:2079-9292
Container-title:Electronics
language:en
Short-container-title:Electronics

Author:

Baek Ji-Young¹,Lee Seok-Pil²^ORCID

Affiliation:

1. Department of Computer Science, Graduate School, Sangmyung University, Seoul 03016, Republic of Korea

2. Department of Intelligent IoT, Sangmyung University, Seoul 03016, Republic of Korea

Abstract

Although emotional speech recognition has received increasing emphasis in research and applications, it remains challenging due to the diversity and complexity of emotions and limited datasets. To address these limitations, we propose a novel approach utilizing DCGAN to augment data from the RAVDESS and EmoDB databases. Then, we assess the efficacy of emotion recognition using mel-spectrogram data by utilizing a model that combines CNN and BiLSTM. The preliminary experimental results reveal that the suggested technique contributes to enhancing the emotional speech identification performance. The results of this study provide directions for further development in the field of emotional speech recognition and the potential for practical applications.

Funder

Sangmyung University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Link

https://www.mdpi.com/2079-9292/12/18/3966/pdf

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