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Pawan Sen
Suman Kumar
Shivam Verma
Dipak Kumar
Adarsh Kumar
Keywords:
Data Analysis, Automation, Machine Learning, Artificial Intelligence, Big Data, Predictive Analytics, Data Speech Recognition, Emotion Detection, Deep Learning, CNN, LSTM, MFCC, Affective Computing, Human-Computer Interaction.
Abstract:
Emotion recognition through speech has gained significant attention in the domains of human-computer interaction, artificial intelligence, and affective computing. This paper explores the development of a speech emotion recognizer using deep learning techniques. By leveraging features like Mel Frequency Cepstral Coefficients (MFCCs) and employing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the proposed system classifies emotions such as happiness, sadness, anger, and neutrality. We compare our model's performance on publicly available datasets such as RAVDESS and CREMA-D. The model achieves high accuracy and demonstrates potential for integration into real-time applications such as virtual assistants, therapy bots, and emotion-aware dialogue systems.
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International Journal of Recent Research and Review
ISSN: 2277-8322
Vol. XVIII, Issue 1
March 2025
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PUBLISHED
March 2025
ISSUE
Vol. XVIII, Issue 1
SECTION
Articles
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