Abstract

Analyzing neonatal vocal expression provides invaluable insights into brain function and the emergence of consciousness, as early vocalization patterns reflect neurodevelopmental trajectories and sensory integration processes. Despite progress in neonatal healthcare, identifying reliable neurological and cognitive markers from infant vocal sounds remains challenging, as it requires linking complex, multi-level brain activity with perceptual acoustic features. This paper reviews methodological approaches used to analyze neonatal vocal expressions, with a focus on techniques that bridge data-driven models with clinical applications. We examine computational methods, including signal processing, feature extraction algorithms, and machine learning models designed to capture vocal biomarkers of neurological or psychiatric disorders. Approaches include spectro-temporal analysis to detect atypical acoustic patterns, deep learning models like convolutional neural networks (CNNs) for automated feature learning, and explainable AI techniques that connect model outputs to clinically interpretable vocal features. We also explore multimodal approaches that combine vocal data with physiological and behavioral signals to improve diagnostic accuracy. The review addresses challenges in neonatal vocal analysis, including data scarcity, demographic variability, and the need for generalization across different recording environments. To mitigate these issues, we highlight advances in domain adaptation, transfer learning, and data augmentation, which enable models to generalize across diverse clinical scenarios. We emphasize the need for clinical validation and interdisciplinary collaboration to ensure practical adoption of these models in healthcare. Future research should focus on refining predictive models with larger, more diverse datasets and enabling real-time analysis for continuous neonatal monitoring. By evaluating existing methodologies and proposing future directions, this study aims to advance neonatal vocal analysis and support early diagnosis and intervention in pediatric healthcare.