Transfer Learning: Speech to Animal Sounds

Updated 22 October 2025

The paper demonstrates that speech-pretrained models, using strategies like fine-tuning and linear probing, effectively recognize animal vocalizations.
Transfer learning from speech to animal sounds relies on domain-invariant features such as MFCC and Chirplet transforms to bridge species-specific acoustic cues.
Empirical results show that few-shot and zero-shot approaches achieve high accuracy across diverse taxa, driving innovation in ecological and bioacoustic applications.

Transfer learning from speech to animal sounds refers to the adaptation of machine learning models—especially deep neural networks—originally optimized for processing human speech so that they can recognize, classify, or extract features from animal vocalizations. This paradigm exploits the shared acoustic structure across vocalizing species and circumvents the demanding data requirements typical in training models for under-resourced bioacoustic tasks. Modern neural representations and pre-training routines, together with bioinspired front-end transformations, support effective cross-domain transfer, enabling advancements in biodiversity monitoring and comparative bioacoustics.

1. Foundations: Audio Representations and Domain-Invariant Features

A common thread across transfer learning research in this context is the reliance on feature representations that encode general acoustic structure, rather than human language–specific cues. Classical approaches utilized Mel Frequency Cepstral Coefficients (MFCC) derived from the perceptual mel scale, recognizing that the human auditory filterbank structure captures both speech and animal vocalizations well (Chalmers et al., 2021). Chirplet-based representations leverage bioinspired time-frequency kernels tuned to modulated transients; the Fast Chirplet Transform (FCT) captures the dynamic spectral contours found in both speech and animal calls, providing a Q-constant, robust, and efficient input space for downstream CNNs (Glotin et al., 2016).

More recently, self-supervised learning (SSL) models—such as HuBERT, WavLM, and Wav2Vec2—are pre-trained on speech with objectives (e.g., masked prediction or contrastive loss) that promote domain-general latent features (Cauzinille et al., 4 Sep 2025, Sarkar et al., 2023). These representations encode amplitude modulation, modulation spectra, source characteristics, and rhythmic patterns relevant to both human and non-human vocal communication. Empirical results confirm that, for many bioacoustic datasets (e.g., marmoset, bat, marine mammals), speech-pretrained SSL features perform comparably, or even outperform, representations trained directly on animal sounds (Sarkar et al., 10 Jan 2025). Classical feature engineering and modern deep SSL approaches thus converge on the principle that domain-invariant, highly-informative features are indispensable for cross-domain transfer.

2. Transfer Learning Strategies: Model Reuse, Fine-Tuning, and Distillation

Transfer learning can be instantiated in several ways. The dominant mode leverages pre-trained weights from a large corpus (e.g., speech, general audio) to initialize models subsequently fine-tuned on animal bioacoustic tasks (Ghani et al., 21 Sep 2024, Ghani et al., 2023). Strategies include:

Shallow fine-tuning: Lower (feature extraction) layers of the neural network are frozen; only classifier heads are retrained. This approach maintains the general acoustic representations, limiting overfitting and reducing computational cost, and is found to be robust in low-data and complex soundscape scenarios (Ghani et al., 21 Sep 2024).
Deep fine-tuning: All neural network layers, including the convolutional or transformer backbone, are adapted to the new dataset. This is beneficial when substantial labeled data from the target domain is available but carries a risk of catastrophic forgetting if the dataset is small or noisy.
Knowledge distillation: A "teacher" model pretrained on one domain (e.g., birdsong via CNN) generates soft predictions (probabilistic or logit-level outputs), which direct the training of an alternative "student" architecture (e.g., transformers) in the target animal domain (Ghani et al., 21 Sep 2024).
Linear probing and time-weighted attention: Pretrained speech models are frozen; either averages (linear probes) or learned attention-weighted sums (time-weighted averaging) of the layer activations are fed to downstream classifiers, enabling rapid prototyping and separating the contribution of episodic and time-aggregated information (Cauzinille et al., 4 Sep 2025).

These methods form the backbone of transfer learning pipelines, supporting both classification and detection applications across taxa.

3. Empirical Evidence and Benchmarking Across Tasks and Taxa

Empirical studies demonstrate robust transferability from speech to animal sounds in numerous contexts:

Voice and Individuality: Deep networks trained for speaker identification transfer to the recognition of voiceprints in chimpanzee and marmoset calls, capturing individual-specific spectral-temporal signatures not limited by call type (Leroux et al., 2021, Sarkar et al., 2023).
Species and Call-Type Classification: CNNs and transformer models pretrained on speech or birdsong generalize to diverse taxa, including birds, bats, frogs, and marine mammals, often matching or exceeding models trained exclusively on animal data (Ghani et al., 2023, Hagiwara et al., 2022). Performance is high even with very limited labeled data—application of few-shot meta-learning and prototypical networks shows that five exemplars can suffice for sound event detection with competitive F-scores (Nolasco et al., 2023, Moummad et al., 2023).
Zero-shot and Cross-Task Generalization: Foundation models (e.g., NatureLM-audio, BioLingual) trained jointly on curated speech, music, and animal sound–language pairs can classify species, detect calls, caption recordings, and count individuals with no explicit retraining, setting state-of-the-art benchmarks for unseen taxa (Robinson et al., 11 Nov 2024, Robinson et al., 2023).
Robustness to Frequency and Noise: Models with noise-robust pretraining (e.g., WavLM, XEUS) maintain performance in heavily contaminated or out-of-band regimes; pitch-shifting experiments suggest minimal degradation for bat calls (Cauzinille et al., 4 Sep 2025). This resilience is attributed to large-scale pretraining on diverse, noise-mixed speech corpora.

Performance is routinely quantified via metrics such as accuracy (species classification), mean Average Precision (detection), F1-score, or Unweighted Average Recall (UAR) (Sarkar et al., 10 Jan 2025, Hagiwara et al., 2022). Cross-domain benchmarks (e.g., BEANS, BEANS-Zero) have standardized comparative assessment and accelerated progress.

4. Bioinspired Front-ends and Universal Acoustic Encodings

Front-end transformations significantly impact transfer performance. Chirplet representations exploit time-varying frequency modulations, capturing the cochlear and auditory cortex’s selectivity for non-stationary, transient-rich vocal signals (Glotin et al., 2016). Fast Chirplet Transforms (FCT), when used to pretrain CNNs, both accelerate training and boost accuracy for bird, whale, and vowel classification, reducing epoch count by 26–28% and raising MAP or accuracy by several percent against Mel or raw audio baselines. These results confirm that bioinspired encodings provide a universal feature basis, which can underpin inter-species transfer.

Feature engineering and optimization in the signal domain—rearrangement, noise reduction with autoencoders, attention-based bidirectional recurrence—further strengthen deep models' ability to generalize from speech to complex animal soundscapes, particularly in low SNR or multi-source conditions (Yang et al., 3 Jul 2024).

5. Interpretable and Language-Based Representations

Novel approaches have reframed animal vocalizations as sequences analogous to text, enabling the reuse of natural LLMs (NLP paradigms) for bioacoustics (Hagiwara et al., 5 Feb 2024). Systems such as ISPA convert animal sounds into discrete symbolic sequences (tokens encoding bandwidth, pitch, duration, slope), yielding compressed "phonetic" streams interpretable by LLMs (RoBERTa). This “foreign language” perspective allows cross-modal transfer: established LLMs, after pretraining on massive human textual corpora, can be applied or fine-tuned for bioacoustic classification, detection, or captioning, moving beyond dense, continuous acoustic features.

Contrastive language-audio pretraining (as in BioLingual) further aligns acoustic and textual representations, facilitating free-text search and zero-shot query of large-scale acoustic archives (Robinson et al., 2023, Robinson et al., 11 Nov 2024). These innovations enable flexible, semantically rich interfaces with ecological data.

6. Applications, Challenges, and Prospects

Transfer learning from speech to animal sounds has direct impact on conservation, behavioral ecology, and biodiversity monitoring:

Automated monitoring: Passive acoustic monitoring systems (using speech-pretrained models) automate detection and classification of birds, frogs, bats, or marine mammals, even in low-resource, noisy, or remote deployments (Chalmers et al., 2021, Ghani et al., 2023).
Few-shot and Zero-shot Learning: Pretrained representations enable rapid adaptation to novel species or call types, providing practical tools for rare or endangered taxa with minimal annotation (Nolasco et al., 2023, Moummad et al., 2023).
Individual and Group Identity: Identity vectors and SSL embeddings facilitate tracing individuals or social groups in primates and social mammals, supporting non-invasive behavioral studies (Leroux et al., 2021, Sarkar et al., 2023).
Sound Synthesis and Conversion: Generative frameworks (e.g., CVAE-based H2NH-VC) now permit realistic synthesis and manipulation of animal vocalizations from human speech or designed audio, supporting applications from scientific playback to creative sound design (Kang et al., 30 May 2025, Hagiwara et al., 2022).

Remaining challenges include domain mismatch in temporal/spectral regularities (e.g., highly non-stationary or ultrasonic signals), the need for annotation standards (multi-species, temporal), and the balance between generalist representations and species/taxa-specialized fine-tuning (Ghani et al., 21 Sep 2024). Domain-specific performance can be enhanced by regularization, supervised contrastive learning (to avoid collapse and maximize transfer), few-shot adaptation, and data augmentation.

Future work aims to extend curriculum learning paradigms, multi-modal integration (audio plus language or vision), more interpretable latent spaces, and ethical/societal consideration in generation and deployment of cross-domain bioacoustic models.

7. Summary Table: Key Methods and Empirical Findings

Method/Model	Speech Pretraining?	Animal-Specific Tuning?	Bioacoustic Test Domains	Key Transfer Result/Metric
Fast Chirplet Transform + CNN	Optional (FCT)	Yes	Birds, Whales, Speech	–28% epochs (Birds), +7.8% MAP (Bird10)
HuBERT, WavLM, XEUS	Yes	Optional/None	Bats, Birds, Mammals	UAR 62‒95%; matches/surpasses animal models
Perch, BirdNET (bird models)	No (birds)	None/Few-shot	Non-bird taxa	ROC-AUC up to .98–.99 even for bats/whales
Prototypical Networks	Possible speech	Few-shot adaptation	Diverse taxa	F-measure to 60+% with only 5 shots
CVAE H2NH-VC	Yes	Non-human reference	Birdsong, Lion, Synt.	MOS-Q/N/S: 3.16/3.16/3.78; SOTA naturalness
BioLingual, NatureLM-audio	Speech/Lang/Music	Universal/fine-tuning	BEANS-Zero, AnimalSpeak	Zero-shot SOTA (>68% Top-1, SOTA on BEANS-Zero)

All listed findings are direct results reported in the referenced studies and their experimental tables.

Transfer learning from speech to animal sounds has matured into a robust strategy for bioacoustic modeling: leveraging shared acoustic structure between domains, it enables resource-efficient, accurate, and scalable solutions for automated monitoring and biological discovery. As representations and models grow in scale and interpretability, the prospects for multi-domain ecological AI continue to expand.