AI can find patterns in animal sounds and connect them with behavior, but it cannot yet translate animal communication into human sentences or let people hold reliable conversations with animals. The strongest recent example is research on sperm-whale calls: scientists have identified repeatable structure in thousands of recordings, not a confirmed dictionary of meanings.
What does it mean for AI to “talk” to animals?
In current research, “talking to animals” means trying to infer what signals do and how they relate to behavior—not asking a model to translate arbitrary animal sounds into human language. AI can help researchers find patterns in large collections of sounds, detect and classify vocalizations, and compare them with movement or social activity. Those capabilities are useful, but identifying a recurring sound pattern does not establish what it means.
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Human-language systems can learn from vast quantities of text for which people already know the words and meanings. Animal communication presents a harder problem: researchers must determine what a sound or behavior means by relating it to context and then testing an interpretation. Olivier Pietquin, AI research director at Earth Species Project, described the challenge to Nature this way: “Understanding is really a tough step.”
What have scientists learned from sperm whales?
A 2024 Nature Communications study, summarized by the Associated Press, analyzed more than 8,700 sperm-whale coda snippets and reported four basic components that may form a kind of phonetic alphabet. CETI founder and president David Gruber told AP, “We’re now starting to find the first building blocks of whale language.”
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Sperm-whale codas are sequences of 3–40 clicks, according to Nature. Whales use them socially at the surface and alongside echolocation during dives. The study’s recurring components are evidence that these signals have structure; they do not provide a validated translation from a particular coda to a human sentence or intention.
The findings also caution against assuming that whale communication works like a fixed list of words. AP quoted lead researcher Pratyusha Sharma saying, “It doesn’t appear that they have a fixed set of codas.” A more flexible system could allow a much larger range of communication, but that possibility is not itself proof of what any specific signal means.
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How do Earth Species Project and Project CETI differ?
Both use AI to investigate animal communication, but their scopes and workflows differ. Earth Species Project describes work toward models that can analyze vocalizations alongside movement and behavior across species. Project CETI concentrates on sperm whales and describes a workflow that pairs machine learning with synchronized observations and behavioral validation.
| Comparison | Earth Species Project | Project CETI |
|---|---|---|
| Species scope | Cross-species research, according to Earth Species Project. | Sperm whales, according to Project CETI. |
| Data and methods | Multimodal foundation models intended to analyze vocalizations alongside movement and behavior; stated goals include denoising, source separation, and automatic detection and classification. (Earth Species Project) | Synchronized hydrophones, suction-cup bio-loggers, drones, video, behavioral observation, and environmental sensors. (Project CETI) |
| Validation described | Not stated in the project description summarized here. (Earth Species Project) | Interpretations are to be validated through playback and peer-reviewed collaboration. (Project CETI) |
| Data and tools | The project says it intends to make data and models available to other research teams. (Earth Species Project) | Not stated in the project description summarized here. (Project CETI) |
| Conservation pathway | Not stated in the project description summarized here. (Earth Species Project) | Not stated in the project description summarized here. (Project CETI) |
Earth Species Project CEO Katie Zacarian described the project’s ambition as moving “what might have been considered science fiction three or four years ago firmly in the realm of the possible.” That is a statement about the research goal, not a claim that a general animal translator already exists. CETI’s focused approach illustrates why collecting sound together with movement and social context matters: the same recording is more informative when researchers can study the circumstances in which it occurred.
Why do researchers need playback and behavioral tests?
A machine-learning system can group similar sounds, identify patterns, or predict what signal may occur in a particular context. But those outputs are not automatically evidence of communication or meaning. A pattern could reflect who made a sound, when it was recorded, or other context rather than a message addressed to another animal.
Researchers therefore need to connect candidate signals with observed behavior and test interpretations. CETI’s stated workflow includes playback: presenting sounds and observing how whales respond. Results also need scrutiny and collaboration, including peer review. Until interpretations survive such tests, it is more accurate to call them candidate patterns or hypotheses than translations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you use an app to speak with a pet or wild animal?
The work described by Earth Species Project and Project CETI does not establish a reliable app that translates arbitrary animal sounds into human language or lets a person converse with a dog, whale, or other wild animal. A sound recorder or classifier may capture or sort vocalizations, but that is different from decoding their meaning. Treat claims of a universal animal-language translator as unverified unless they show how meanings were validated against animal behavior.
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Hydrophones are underwater microphones used to record sounds such as whale calls; Project CETI reports using synchronized hydrophones as part of its data collection, along with bio-loggers, drones, and environmental sensors. A hydrophone recorder can help capture signals for observation and analysis. It does not translate arbitrary animal communication, and a recording alone cannot establish what a signal means.
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