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.

翻译：在幼年动物如家禽雏鸡（Gallus gallus）中，鸣叫声传递着情感状态与行为状态的信息。依赖人工标注和预定义分类的传统鸣叫分析方法存在主观偏差、可扩展性有限，且无法捕捉鸣叫库的全部复杂性。我们提出了一种用于雏鸡鸣叫声自动检测、声学特征提取和无监督学习的计算框架。将该框架应用于新孵化雏鸡的数据集，我们识别出两个主要鸣叫聚类。随后，我们在独立数据集上测试了该计算框架，该数据集包含胚胎发育期间暴露于溶剂对照或丙戊酸（VPA）的雏鸡——丙戊酸是一种干扰神经发育并与自闭症样症状相关的化合物。对实验数据集的聚类分析确认了两个主要鸣叫聚类，并揭示了组间系统性差异。VPA暴露雏鸡表现出改变的鸣叫库，其轻柔叫声相对增加。VPA对不同鸣叫聚类的影响存在差异，调控了时域、频域和能量域特征。总体而言，VPA暴露雏鸡产生的鸣叫声持续时间更短、音高变异性降低、能量分布改变，且在较响亮叫声中观察到最显著的变化。本研究为分析动物鸣叫声提供了计算框架，增进了对典型与非典型发声发育早期生命交流的认识。