Decoding Poultry Vocalizations - Natural Language Processing and Transformer Models for Semantic and Emotional Analysis

Abstract

Deciphering the acoustic "language" of chickens opens new frontiers in animal welfare and ecological informatics, illuminating how subtle vocal signals encode health status, emotional states, and interactions within ecosystems. By uncovering the semantics of these vocalizations, we gain a powerful tool for interpreting their functional vocabulary--how each call serves a purpose within the social and environmental context. Here, we leverage state-of-the-art Natural Language Processing (NLP) and transformer-based models to translate bioacoustic data into meaningful insights. Our approach integrates Wave2Vec 2.0 for raw audio feature extraction with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, pretrained on a broad corpus of animal sounds and adapted to poultry-specific tasks. This novel pipeline decodes poultry vocalizations into interpretable categories--such as distress calls, feeding signals, and mating vocalizations--while revealing subtle emotional nuances often overlooked by traditional spectrogram-based analyses. Achieving 92% accuracy in classifying key vocalization types, our methodology demonstrates the feasibility of real-time, automated monitoring of flock health and stress levels. By continuously tracking this functional vocabulary, farmers can respond proactively to environmental or behavioral changes, enhancing poultry welfare, reducing stress-induced productivity losses, and promoting more sustainable farm management. Beyond its direct agricultural applications, this work enriches our understanding of computational ecology. Gaining access to the semantic foundation of animal calls provides a window into the ecological networks of which poultry are a part, potentially serving as indicators of biodiversity, environmental stressors, and species interactions. In bridging animal behavior, machine learning, and ecosystem analysis, our framework lays a foundation for integrative studies that harness acoustic data to inform ecological decision-making and develop more resilient, ethically aligned agricultural systems.