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Deep Learning for Emotion Detection in Speech: Techniques and Applications
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Author(s): Nipurn Bhaal (SRM Institute of Science and Technology, India), G. Usha (SRM Institute of Science and Technology, India), Ananya Verma (SRM Institute of Science and Technology, India)and S. Aruna (SRM Institute of Science and Technology, India)
Copyright: 2024
Pages: 32
Source title:
Machine and Deep Learning Techniques for Emotion Detection
Source Author(s)/Editor(s): Mritunjay Rai (Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, India)and Jay Kumar Pandey (Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, India)
DOI: 10.4018/979-8-3693-4143-8.ch006
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Abstract
This chapter delves into the pivotal role of deep learning-based emotion detection in speech, shaping human-computer interactions. The authors guide readers through crucial stages, encompassing data collection, preprocessing, feature extraction, model architecture selection, and performance evaluation. Exploring diverse deep learning architectures like CNNs, RNNs, and CRNNs, the chapter highlights their efficacy in decoding sequential speech patterns. Practical aspects, including fine-tuning parameters and real-time optimization, enhance efficiency. Ethical considerations, addressing privacy and data biases, ensure responsible deployment. Real-world applications spanning human-computer interaction, customer service, and mental health underscore the transformative impact of deep learning in daily life. This chapter offers a comprehensive exploration of applying deep learning techniques to analyze emotions in speech, catering to researchers and practitioners alike.
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