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 dimen- sionality 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 com- binatorial complexity. Analysis of MRs and syllabic transition networks revealed that mother-pupil interactions were characterized by repetitions, while commu- nication 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.

翻译：本研究提出一种无监督方法，用于推断果蝠发声的离散性、句法及时序结构，作为分级发声系统的案例研究，并评估通信模式相对于行为情境的复杂性。该方法通过探究梅尔频谱图上的降维如何影响标注，并与基于声学相似性的无监督标注进行比较，改进了基于流形学习的发声单元（即音节）无监督标注基线。随后，我们将发声编码为音节序列以分析句法类型，并提取最大重复模式（MRs）以评估句法结构。研究发现证据表明：i) 存在关联句法而非组合句法（情境分类不受序列排列影响，F1 > 0.9）；ii) 音节使用具有情境依赖性（Wilcoxon秩和检验，p值 < 0.05）；iii) MRs呈重尾分布（截断幂律分布，指数α < 2），暗示编码组合复杂性的机制。对MRs和音节转移网络的分析显示，母幼互动以重复模式为特征，而冲突情境下的通信比非对抗情境表现出更高复杂性（更长的MRs和更互联的发声序列）。我们认为，意见分歧场景中的通信复杂性更高，这反映了信息可压缩性的降低。