Deep Learning for Emotion Detection in Speech

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.