Associative Syntax and Maximal Repetitions reveal context-dependent complexity in fruit bat communication
- URL: http://arxiv.org/abs/2512.01033v1
- Date: Sun, 30 Nov 2025 19:01:59 GMT
- Title: Associative Syntax and Maximal Repetitions reveal context-dependent complexity in fruit bat communication
- Authors: Luigi Assom,
- Abstract summary: This study presents an unsupervised method to infer discreteness, syntax and temporal structures of fruit-bats vocalizations.<n>It evaluates the complexity of communication patterns in relation with behavioral context.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- SMILE: A Composite Lexical-Semantic Metric for Question-Answering Evaluation [55.26111461168754]
We introduce SMILE: Semantic Metric Integrating Lexical Exactness, a novel approach that combines sentence-level semantic understanding with keyword-level semantic understanding and easy keyword matching.<n>It is highly correlated with human judgments and computationally lightweight, bridging the gap between lexical and semantic evaluation.
arXiv Detail & Related papers (2025-11-21T17:30:18Z) - DEPTH: Hallucination-Free Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement [22.164114662885652]
DEPTH is a framework that integrates Dependency-aware sEntence simPlification and Two-tiered Hierarchical refinement into the relation extraction pipeline.<n>We show that DEPTH reduces the average hallucination rate to 7.0% while achieving a 17.2% improvement in average F1 score over state-of-the-art baselines.
arXiv Detail & Related papers (2025-08-20T03:35:24Z) - CORG: Generating Answers from Complex, Interrelated Contexts [57.213304718157985]
In a real-world corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors.<n>Previous research has shown that language models struggle with these complexities, typically focusing on single factors in isolation.<n>We introduce Context Organizer (CORG), a framework that organizes multiple contexts into independently processed groups.
arXiv Detail & Related papers (2025-04-25T02:40:48Z) - Learning Disentangled Speech Representations [0.412484724941528]
SynSpeech is a novel large-scale synthetic speech dataset designed to enable research on disentangled speech representations.<n>We present a framework to evaluate disentangled representation learning techniques, applying both linear probing and established supervised disentanglement metrics.<n>We find that SynSpeech facilitates benchmarking across a range of factors, achieving promising disentanglement of simpler features like gender and speaking style, while highlighting challenges in isolating complex attributes like speaker identity.
arXiv Detail & Related papers (2023-11-04T04:54:17Z) - Unsupervised Mismatch Localization in Cross-Modal Sequential Data [5.932046800902776]
We develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal data.
We propose a hierarchical Bayesian deep learning model, named mismatch localization variational autoencoder (ML-VAE), that decomposes the generative process of the speech into hierarchically structured latent variables.
Our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations.
arXiv Detail & Related papers (2022-05-05T14:23:27Z) - Visualizing Classifier Adjacency Relations: A Case Study in Speaker
Verification and Voice Anti-Spoofing [72.4445825335561]
We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers.
Based upon rank correlations, our method facilitates a visual comparison of classifiers with arbitrary scores.
While the approach is fully versatile and can be applied to any detection task, we demonstrate the method using scores produced by automatic speaker verification and voice anti-spoofing systems.
arXiv Detail & Related papers (2021-06-11T13:03:33Z) - Syntactic Perturbations Reveal Representational Correlates of
Hierarchical Phrase Structure in Pretrained Language Models [22.43510769150502]
It is not entirely clear what aspects of sentence-level syntax are captured by vector-based language representations.
We show that Transformers build sensitivity to larger parts of the sentence along their layers, and that hierarchical phrase structure plays a role in this process.
arXiv Detail & Related papers (2021-04-15T16:30:31Z) - Syntactic representation learning for neural network based TTS with
syntactic parse tree traversal [49.05471750563229]
We propose a syntactic representation learning method based on syntactic parse tree to automatically utilize the syntactic structure information.
Experimental results demonstrate the effectiveness of our proposed approach.
For sentences with multiple syntactic parse trees, prosodic differences can be clearly perceived from the synthesized speeches.
arXiv Detail & Related papers (2020-12-13T05:52:07Z) - Pareto Probing: Trading Off Accuracy for Complexity [87.09294772742737]
We argue for a probe metric that reflects the fundamental trade-off between probe complexity and performance.
Our experiments with dependency parsing reveal a wide gap in syntactic knowledge between contextual and non-contextual representations.
arXiv Detail & Related papers (2020-10-05T17:27:31Z) - Continuous speech separation: dataset and analysis [52.10378896407332]
In natural conversations, a speech signal is continuous, containing both overlapped and overlap-free components.
This paper describes a dataset and protocols for evaluating continuous speech separation algorithms.
arXiv Detail & Related papers (2020-01-30T18:01:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.