Ensemble Learning-Based Speech Emotion Recognition
for Children with Autism Spectrum Disorder: A Solution
for Emotion Identification
Volume: 13 - Issue: 05 - Date: 01-05-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU382 | Page No.: 101-107
Author: Rajat Tiwari
Co- Author: Dr. Sneha Soni
Abstract:-This paper presents Speech emotion recognition is an important field of study aimed at developing
systems capable of automatically identifying and classifying emotions from speech signals. In this research,
we propose a deep learning-based approach using LSTM (Long Short-Term Memory) neural networks for
speech emotion recognition. The study utilizes a dataset of speech recordings with labeled emotion
annotations. The preprocessing stage involves segmenting the speech data into frames and extracting
relevant acoustic features like Mel-frequency cepstral coefficients (MFCCs).
The LSTM architecture is designed to capture temporal dependencies and patterns in the speech data.
Experimental results demonstrate the effectiveness of the proposed method, achieving an accuracy of 85% in
classifying emotions such as happiness, sadness, anger, and neutral. The findings indicate the potential of
deep learning and LSTM models in accurately recognizing emotions from speech signals.
Further improvements and future research directions are discussed, including fine-tuning techniques and
real-time deployment. The proposed method contributes to the advancement of speech emotion recognition
systems, which have applications in fields such as affective computing, human-computer interaction, and
healthcare.
Key Words:-Autism Spectrum Disorder, Speech Emotion Recognition, Ensemble Learning, Human
Emotion Identification, Social Interaction, Communication Skills.
Area:-Engineering
Download Paper:
Preview This Article
