S. Tyagi, S. Szénási: Advancements and Challenges in Emotion Extraction from Speech: A PRISMA-Guided Systematic Review of Machine and Deep Learning Technique. In Advances in Service and Industrial Robotics (Mechanisms and Machine Science). Cham, CH : Springer, pp. 439–447, 2025. ISSN 2211-0984, ISBN 978-3-032-02105-2 link

Abstract: This PRISMA-compliant systematic review synthesizes 127 studies (2018–2023) on emotion extraction from speech, focusing on machine learning (ML) and deep learning (DL) advancements. We analyze traditional methods (SVMs, HMMs), DL architectures (CNNs, transformers), and emerging trends (self-supervised learning, multimodal fusion). Our methodology includes rigorous quality assessment, meta-analysis, and bias evaluation. Results reveal DL models achieve 85% accuracy on IEMOCAP, surpassing ML’s plateau at 68%, but critical gaps persist in cultural bias, reproducibility, and ethics. We propose standardized benchmarks and ethical frameworks for future research. This review serves as a comprehensive guide for researchers and practitioners in affective computing.