用无监督方法量化鸟类发声随发育变化的可塑性。
Trajectory Variance: An Unsupervised Measure of Developmental Vocal Plasticity in Birdsong
- 通过潜空间位移模型预测不同日龄下的发声变化轨迹。
- 实验证明该方法能有效区分学习型鸣叫与先天叫声。
- 适合研究动物发声发育、神经机制或语音可塑性的研究者。
发声在发育过程中会如何变化？我们提出轨迹方差，一种无需类型标签的每条发声可塑性评分方法。通过一个位移模型学习预测自编码器潜空间中年龄条件下的变化；其对目标日龄预测结果的方差，量化了每条发声若在不同发育阶段发出可能产生的变化量。在三只斑胸草雀（18.3万至27.4万次发声，40至101天日龄）上评估，轨迹方差在控制时长后能有效区分学习型鸣唱与先天叫声（Cohen's d = 0.29-0.57，AUC = 0.58-0.67），而所有非参数基线均未表现出一致分离能力。轨迹方差还与三只鸟的频谱平坦度显著相关（r = -0.48 至 -0.75）：可塑性越强的发声，其频谱越具音调性与结构性。
原文摘要 · Abstract (English)
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.
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