Audio and Speech Intelligence Using Deep Learning for Recognition and Emotion Analysis
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This chapter provides a comprehensive exploration of Audio and Speech Intelligence, with a specific focus on the application of deep learning for emotion recognition and analysis. We delve into the foundational concepts of Speech Emotion Recognition (SER), tracing its evolution from traditional machine learning paradigms to the current state-of-the-art deep learning models. The chapter introduces key deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid models, and examines their effectiveness in capturing the complex patterns of emotional speech. We propose a novel CNN-LSTM hybrid model and evaluate its performance on the RAVDESS and TESS emotional speech datasets. The Results and Discussions section provides a detailed analysis of the model’s performance, including accuracy, precision, recall, and F1-score, and visualizes the results through confusion matrices and training curves. Finally, we conclude with a summary of our findings and a discussion of future research directions in this rapidly evolving field.
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Deep Learning: Foundations, Advances, and Intelligent Applications
