Emotion recognition for human–computer interaction using high-level descriptors

Recent research has focused extensively on employing Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNN), for Speech Emotion Recognition (SER). This study addresses the burgeoning interest in leveraging DL for SER, specifically focusing on Punjabi language speakers. The p...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 12122 - 12
Hauptverfasser: Singla, Chaitanya, Singh, Sukhdev, Sharma, Preeti, Mittal, Nitin, Gared, Fikreselam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 27.05.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
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692/700
Algorithms
Bayes Theorem
Computer engineering
Datasets
Deep Learning
Emotion recognition
Emotions
Emotions - physiology
Females
Happiness
High-level features
Humanities and Social Sciences
Humans
Language
multidisciplinary
Neural networks
Neural Networks, Computer
Punjabi database
Punjabi speech emotion recognition
Science
Science (multidisciplinary)
Social Media
Social networks
Speech
Speech emotion recognition (SER)
Deep learning
Punjabi database
Emotion recognition
Punjabi speech emotion recognition
Speech emotion recognition (SER)
High-level features
ISSN:2045-2322, 2045-2322
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Beschreibung
Zusammenfassung:Recent research has focused extensively on employing Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNN), for Speech Emotion Recognition (SER). This study addresses the burgeoning interest in leveraging DL for SER, specifically focusing on Punjabi language speakers. The paper presents a novel approach to constructing and preprocessing a labeled speech corpus using diverse social media sources. By utilizing spectrograms as the primary feature representation, the proposed algorithm effectively learns discriminative patterns for emotion recognition. The method is evaluated on a custom dataset derived from various Punjabi media sources, including films and web series. Results demonstrate that the proposed approach achieves an accuracy of 69%, surpassing traditional methods like decision trees, Naïve Bayes, and random forests, which achieved accuracies of 49%, 52%, and 61% respectively. Thus, the proposed method improves accuracy in recognizing emotions from Punjabi speech signals.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-59294-y