# Discovering Vocal Chunks in Birdsong Using Language Model Tokenizers

Axel Arnaud, Xavier Hinaut

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Axel Arnaud, Xavier Hinaut. Discovering Vocal Chunks in Birdsong Using Language Model Tokenizers. ICDL 2025 - IEEE International Conference on Development and Learning, Sep 2025, Prague, Czech Republic. 10.5281/zenodo.6521932. hal-05160051

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https://inria.hal.science/hal-05160051v1

Submitted on 12 Jul 2025

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# Discovering Vocal Chunks in Birdsong
Using Language Model Tokenizers

Axel Arnaud<fnref n="1" /> and Xavier Hinaut<fnref n="1" />

## I. ABSTRACT

Chunking refers to the process of grouping elements into coherent units that can be reused at higher hierarchical levels. This principle is central in cognitive science and underpins modern NLP, where Large Language Models (LLMs) rely on fixed token sets generated by subword segmentation algorithms such as Byte Pair Encoding (BPE) or WordPiece. These tokenizers yield compressed, reusable vocabularies that support efficient learning and generalization. Interestingly, songbirds provide a biologically grounded model of vocal sequence learning, with structured songs composed of motifs, syllables, and phrases that resemble linguistic chunks. In this study, we investigate whether statistical tokenization methods developed for language can identify meaningful chunks in canary song sequences. Our goals are to (1) compare tokenizer-based chunking with expert-defined segmentation, (2) assess alignment between discovered chunks and neural representations in premotor areas (e.g., HVC), and (3) use chunk-based representations to improve sensorimotor models of song production. We propose that subword tokenization not only offers a new tool for analyzing birdsongs, but also sheds light on the emergence of hierarchical structure in vocal learners.

## II. INTRODUCTION

We previously proposed a full vocal sensorimotor model [1] able to learn to produce high quality birdsong syllables. The canonical model is composed of 3 modules: a motor control model (for sound generation), a perceptual model and an inverse model. To test the ability of the perceptual and motor representations to facilitate learning, we used a simple hebbian learning rule (i.e. co-activation of motor and perceptual areas) for the inverse model. It had to map perceptual representations (e.g. a representation given the sound of a syllable) to motor representations (e.g. a representation of the motor command generating the corresponding syllable): thus, to learn the inverse mapping. Surprisingly, the model was able to learn to produce most syllables after training even with a simple hebbian learning rule. Now, we would like to extend this single-syllable model to song generation. We aim to find compressed sequence representations that enable learning with simple learning rules.

## III. METHODS

We use annotated domestic canary songs from various canaries, including one dataset already published [2]. Human annotations were cleaned using our Canapy library: github.com/birds-canopy/canapy We compare in-house canary song chunking algorithms, subword tokenizers (BPE, WordPiece, ...) and Probabilistic Suffix Trees (PST) [3] and apply them to various canary datasets. Additionally, we explore which hyperparameter settings yield coherent results.

## IV. RESULTS

Three tokenizers were assessed: BPE, WordPiece and Unigram [4]. We used the so called "Elbow method" to determine the best vocabulary size (the given number of tokens a tokenizer provides). WordPiece and Unigram, given the right vocabulary size, were able to find all of the chunks found by in-house algorithm, by hand, and most of the chunks found by PST — i.e. the sequences common to PSTs computed by convergence and divergence probabilities [5].

## V. DISCUSSION

In future work, we want to compare the methods of this study with other chunking methods recently proposed as human hierarchical chunking model [6]. Moreover, we have recorded brain data while canaries listen to their own song or modified versions of it. These recordings are from the HVC area: a premotor region supposed to encode the sequence motor program of song, but also sentitive to auditory stimuli such as bird own song and songs from conspecifics. It is of particular interest for sensorimotor models because mirror neurons have been observed in HVC area [7].

## REFERENCES

[1] S. Pagliarini, A. Leblois, and X. Hinaut, "Canary vocal sensorimotor model with rnn decoder and low-dimensional gan generator," in *ICDL 2021*, Beijing, China, Aug. 2021.

[2] J. Giraudon, N. Trouvain, A. Cazala, C. Del Negro, and X. Hinaut, "Labeled songs of domestic canary m1-2016-spring (serinus canaria)," May 2021. [Online]. Available: https://doi.org/10.5281/zenodo.6521932

[3] J. E. Markowitz et al., "Long-range order in canary song," *PLOS Computational Biology*, vol. 9, no. 5, pp. 1-12, 05 2013.

[4] T. Kudo, "Subword regularization: Improving neural network translation models with multiple subword candidates," 2018.

[5] K. E. Bouchard and M. S. Brainard, "Neural encoding and integration of learned probabilistic sequences in avian sensory-motor circuitry," *Journal of Neuroscience*, vol. 33, no. 45, pp. 17710-17723, 2013.

[6] S. Wu et al., "Learning structure from the ground up—hierarchical representation learning by chunking," *NeurIPS*, vol. 35, pp. 36706-36721, 2022.

[7] N. Giret et al., "Evidence for a causal inverse model in an avian cortico-basal ganglia circuit," *PNAS*, vol. 111, no. 16, pp. 6063-6068, 2014.

<footnote>*This study has received financial support from the French government in the framework of the France 2030 programme IdEx université de Bordeaux RRI ROBSYS, from the regional robotics network R4 the Nouvelle-Aquitaine region, France, and from the French ANR Grant DeepPool ANR-21-CE23-0009.</footnote>

<footnote n="1">¹ AA: Inria, Labri, IMN aarnaud010@ensc.fr</footnote>

<footnote n="1">¹ XH: Inria, Labri, IMN xavier.hinaut@inria.fr</footnote>