Abstract Cattle express their physiological and emotional states through vocalizations, often long before visible behavioral symptoms emerge. This review critically examines the evolution of artificial intelligence (AI) techniques used to decode these vocal signals, tracing the development from early signal processing and classical machine learning approaches to contemporary deep learning architectures and large language models (LLMs). Drawing from a systematic analysis of over 120 core studies, we evaluate the capabilities, limitations, and real-world applicability of current methods, highlighting persistent challenges such as data scarcity, limited cross-farm generalizability, and a lack of interpretability in black-box models. The integration of multimodal sensor data—including audio, accelerometry, thermal imaging, and environmental inputs—emerges as a pivotal strategy for achieving accurate, context-aware, and real-time welfare assessment.We propose a Hybrid Explainable Acoustic Multimodal (HEAM) model, which fuses spectrogram-based convolutional neural networks (CNNs), interpretable decision trees, and natural language reasoning modules to generate transparent and actionable alerts for farmers. In addition to surveying technical progress, the review explores ethical considerations, such as anthropomorphism, data privacy, and the potential misuse of AI in welfare decisions. Best practices for dataset curation, cross-farm validation, and model explainability are also outlined. By shifting animal welfare monitoring from intermittent human observation to continuous, sensor-driven, animal-centered analysis, AI-enabled bioacoustics holds promise for earlier disease detection, improved treatment outcomes, enhanced productivity, and increased societal trust in precision livestock farming.
Kate et al. (2025) studied this question.