Emotion Analysis Using Improved Cat Swarm Optimization with Machine Learning for Speech-impaired People - Journal Article

Aug 4, 2026

Emotion analysis enhances interaction and emotional well-being for speech-impaired people by extracting emotion from speech. The approach leverages machine learning and deep learning classifiers such as random forests, deep neural networks, and support vector machines, while accounting for individual differences and context during interpretation. Because sensitive speech data requires protection, the study emphasizes privacy, data security, and informed consent. It proposes EA-ICSOML using improved cat swarm optimization, combines computer vision and deep learning for emotion identification, generates feature vectors with ShuffleNet, tunes hyperparameters via ICSO, and classifies emotions using a transient chaotic neural network validated on facial emotion databases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Emotion detection from facial and speech expressions

Challenges for facial emotion recognition

Proposed EA-ICSOML emotion analysis approach

Journal of Disability Research 2024 | Volume 3 | Pages: 1–9 | e-location ID: e20240017 DOI: 10.57197/JDR-2024-0017 Emotion Analysis Using Improved Cat Swarm Optimization with Machine Learning for Speech-impaired People Haya Mesfer Alshahrani1 ,* , Ishfaq Yaseen2 and Suhanda Drar2 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia 2Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia Correspondence to: Haya Mesfer Alshahrani*, e-mail: [hmalshahrani@pnu.edu.sa](hmalshahrani@pnu.edu.sa) Received: May 24 2023; Revised: October 16 2023; Accepted: February 20 2024; Published Online: March 28 2024 ABSTRACT Emotion analysis is an effective method for improving interaction and understanding for speech-impaired people. We can provide the best interaction and support emotional well-being by analyzing the emotion conveyed through speech. Using deep learning (DL) or machine learning algorithms for training an emotion classification method. This might include training classifiers namely random forests, deep neural networks, or support vector machines. It is noteworthy that emotion analysis could be effective; however, it is crucial to consider individual differences and context while interpreting emotion. Furthermore, ensuring data protection and privacy and obtaining consent are vital features to consider while working with sensitive speech data. Therefore, this study presents an emotion analysis approach using improved cat swarm optimization with machine learning (EA-ICSOML) technique. The EA-ICSOML technique applies the concepts of computer vision and DL to identify various types of emotions. For feature vector generation, the ShuffleNet model is used in this work. To adjust the hyperparameters compared to the ShuffleNet system, the ICSO algorithm is used. Finally, the recognition and classification of emotions are performed using the Transient Chaotic Neural Network approach. The performance validation of the EA-ICSOML technique is validated on facial emotion databases. The simulation result inferred the improved emotion recognition results of the EA-ICSOML approach compared to other recent models in terms of different evaluation measures. KEYWORDS computer vision, emotion recognition, machine learning, deep learning, speech-impaired people INTRODUCTION Emotion can be described as a mental state linked with the nervous system; that is, what an individual feels inside as the effects of the environment. The emotions of a person can be identified in many ways (Nandwani and Verma, 2021) . Some can be examined by body gestures, tonal properties, and facial expressions. The classification or computing of emotions from facial or speech expressions formed a significant part of human information processing (Ahire and Borse, 2022) . In the intellectual learning environment, emotion detection of learners’ images during class hours using computer and deep learning (DL) methods enables prompt monitoring of the emotional and psychological states of learners. Emotion detection using facial expression images needs high-quality cameras for capturing facial images, resulting in high application costs (Zad et al., 2021) . Hence, the speech-related human emotion detection approach has slowly become the principal approach to studying human–computer emotion detection. In expression and communication, the speech of humans does not have semantic data but implies rich data like the emotions of speakers (Sailunaz et al., 2018) . Thus, the study of emotion detection related to image and human speech using computer and intellectual methods of DL is of great significance (Vasantharajan et al., 2022) . Automated emotion detection is a significant research study that solves two subjects: artificial intelligence and human emotion recognition. The emotional

FAQs

How does the study improve interaction for speech-impaired people?

It improves interaction by analyzing emotions conveyed through speech to provide suitable support for emotional well-being.

What is EA-ICSOML and what roles do ShuffleNet and ICSO play?

EA-ICSOML is an emotion analysis approach using improved cat swarm optimization with machine learning. ShuffleNet generates feature vectors, while ICSO adjusts hyperparameters for better performance.

How are emotions classified in the proposed method?

Emotion recognition and classification are performed using a transient chaotic neural network.

Aug 4, 2026

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