发表机构
Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学和瑞士联邦理工学院神经信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究鸟类鸣叫发育中发声变化，提出轨迹方差这一可塑性得分，用位移模型预测自动编码器潜在空间中年龄条件变化，其预测方差量化发声变化，能区分习得音节与先天鸣叫且与频谱平坦度相关。
AI 中文摘要
发声在发育过程中变化多少？我们提出轨迹方差，一种无需类型标签就能回答此问题的发声可塑性得分。位移模型学习预测自动编码器潜在空间中年龄条件变化；其预测在目标年龄间的方差量化了若在不同发育阶段产生每个发声会变化多少。在三只斑胸草雀上评估，轨迹方差能区分习得歌曲音节与先天鸣叫，且与所有三只鸟的频谱平坦度相关。
英文摘要
How much does a vocalization change over the course of development? We propose trajectory variance, a per-vocalization plasticity score that answers this question without type labels. A displacement model learns to predict age-conditioned shifts in autoencoder latent space; the variance of its predictions across target ages quantifies how much each vocalization would change if produced at different developmental stages. Evaluated on three zebra finches (183K-274K vocalizations, 40-101 days post-hatch), trajectory variance separates learned song syllables from innate calls (Cohen's d = 0.29-0.57, AUC = 0.58-0.67, after controlling for duration), while no nonparametric baseline achieves consistent separation. Trajectory variance also correlates with spectral flatness across all three birds (r = -0.48 to -0.75): more plastic vocalizations tend to have more tonal, structured spectra.
Journal refInterspeech 2026
DOI：10.21437/Interspeech.2026-3557
