End-to-End Emotion Recognition from Raw Audio: Speaker-Aware, Noise-Resilient, and Multimodal Adaptive Learning Approaches

Abstract: Emotion recognition from speech is a cornerstone of next-generation human-computer interaction, social robotics, and healthcare technologies. While traditional approaches have relied heavily on handcrafted acoustic features like Mel-Frequency Cepstral Coefficients (MFCCs), recent advances in deep learning have shifted the paradigm toward end-to-end models that process raw audio waveforms directly. However, significant challenges remain, including speaker variability, environmental noise, and the limited contextual understanding inherent in unimodal systems. This paper proposes a comprehensive hybrid framework that integrates speaker-aware modeling, noise-resilient architectures, and adaptive multimodal learning — combining audio, text, and video modalities. By critically synthesizing recent empirical findings and through scientific modeling, we offer novel interpretations and propose scalable solutions that enhance accuracy, robustness, and real-world applicability in noisy, speaker-diverse environments. Keywords: Emotion Recognition, Raw Audio Processing, End-to-End Deep Learning, Speaker-Aware Models, Noise-Resilient Learning, Multimodal Fusion, Audiovisual Sentiment Analysis, Deep Neural Networks, Human-Computer Interaction, Adaptive Multimodal Systems