Embryonic Exposure to VPA Influences Chick Vocalisations: A Computational Study

TL;DR

This study introduces a computational framework to analyze chick vocalizations, revealing that embryonic VPA exposure alters vocal repertoire features, providing insights into early communication and neurodevelopmental effects.

Contribution

The paper presents a novel automated method for detecting and classifying chick vocalizations, demonstrating its effectiveness in identifying effects of VPA exposure on vocal features.

Findings

VPA-exposed chicks show increased softer calls.

VPA alters call duration, pitch variability, and energy profiles.

Two primary vocal clusters are identified and affected by VPA.

Abstract

In young animals like poultry chicks (Gallus gallus), vocalisations convey information about affective and behavioural states. Traditional approaches to vocalisation analysis, relying on manual annotation and predefined categories, introduce biases, limit scalability, and fail to capture the full complexity of vocal repertoires. We introduce a computational framework for the automated detection, acoustic feature extraction, and unsupervised learning of chick vocalisations. Applying this framework to a dataset of newly hatched chicks, we identified two primary vocal clusters. We then tested our computational framework on an independent dataset of chicks exposed during embryonic development to vehicle or Valproic Acid (VPA), a compound that disrupts neural development and is linked to autistic-like symptoms. Clustering analysis on the experimental dataset confirmed two primary vocal clusters and revealed systematic differences between groups. VPA-exposed chicks showed an altered repertoire, with a relative increase in softer calls. VPA differentially affected call clusters, modulating temporal, frequency, and energy domain features. Overall, VPA-exposed chicks produced vocalisations with shorter duration, reduced pitch variability, and modified energy profiles, with the strongest alterations observed in louder calls. This study provides a computational framework for analysing animal vocalisations, advancing knowledge of early-life communication in typical and atypical vocal development.