arXiv:2512.01033cs.LGcs.CL2025-12中稿 · NeurIPS

通过分析果蝠叫声中的重复模式和语法结构,发现冲突情境下交流更复杂。

Associative Syntax and Maximal Repetitions reveal context-dependent complexity in fruit bat communication

  • 用降维与声学相似性结合方法自动标注叫声单元,提升无监督标记效果
  • 发现叫声具有关联性语法而非组合式结构,且不同情境使用不同叫声
  • 冲突场景中叫声重复更长、网络更复杂,体现信息不可压缩性

本研究提出一种无监督方法,用于推断果蝠叫声的离散性、语法及时间结构,作为连续发声系统的案例,评估其在不同行为情境下的交流复杂性。该方法通过流形学习改进了基线的叫声单元(音节)无监督标注,考察梅尔频谱图降维对标注的影响,并与基于声学相似性的无监督标签进行比较。随后将叫声编码为音节序列以分析语法类型,提取最大重复(MRs)评估语法结构。结果表明:一、存在关联性语法而非组合式语法(序列顺序打乱后分类准确率仍高于0.9);二、叫声使用受情境影响(威尔科克森秩和检验,p < 0.05);三、MR呈重尾分布(截断幂律,指数α < 2),反映组合复杂性编码机制。对MR与音节转移网络的分析显示,母子互动以重复为主,而冲突情境下的交流比非攻击性情境更复杂(更长的MR、更多连接的语音序列)。我们推测,分歧情境中沟通复杂度更高,反映信息不可压缩性。

原文摘要 · Abstract (English)

This study presents an unsupervised method to infer discreteness, syntax and temporal structures of fruit-bats vocalizations, as a case study of graded vocal systems, and evaluates the complexity of communication patterns in relation with behavioral context. The method improved the baseline for unsupervised labeling of vocal units (i.e. syllables) through manifold learning, by investigating how dimensionality reduction on mel-spectrograms affects labeling, and comparing it with unsupervised labels based on acoustic similarity. We then encoded vocalizations as syllabic sequences to analyze the type of syntax, and extracted the Maximal Repetitions (MRs) to evaluate syntactical structures. We found evidence for: i) associative syntax, rather than combinatorial (context classification is unaffected by permutation of sequences, F 1 > 0.9); ii) context-dependent use of syllables (Wilcoxon rank-sum tests, p-value < 0.05); iii) heavy-tail distribution of MRs (truncated power-law, exponent α < 2), indicative of mechanism encoding combinatorial complexity. Analysis of MRs and syllabic transition networks revealed that mother-pupil interactions were characterized by repetitions, while communication in conflict-contexts exhibited higher complexity (longer MRs and more interconnected vocal sequences) than non-agonistic contexts. We propose that communicative complexity is higher in scenarios of disagreement, reflecting lower compressibility of information.

动物交流语音分析复杂性

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