Machine learning has not translated animal languages into English. It has made animal sounds easier to search, compare and test—and that has helped researchers find evidence that elephants use calls directed at particular individuals. Playback experiments suggest elephants respond differently to calls addressed to them than to calls meant for another elephant. These are promising, carefully tested name-like signals, not proof of an elephant dictionary.
What would count as an animal “name”?
A sound that identifies its caller is not necessarily a name. Animals can have distinctive voices, and a model may learn to recognize which individual produced a call. That is different from a caller using a signal aimed at, or referring to, another individual.
Evidence for a name-like vocal label would be stronger if a signal were associated with a particular recipient, distinguishable from signals directed to others, and recognized by that recipient in a way that changes its behavior. Researchers also need to consider whether the sound simply imitates the recipient’s own call. Even when several of these tests are met, “name-like” is safer than assuming the signal works like a human proper noun.
It helps to separate five questions: Who made the sound? Who was it directed to? What does it refer to? What does the recipient infer? And does the communication system have the structured, shared conventions associated with language? Machine learning can directly assist with the first two. The later questions need evidence about animal behavior and context.
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From a wall of sound to testable patterns
Wildlife recordings are not neat collections of isolated calls. Wind, rain, boats, insects and other animals can mask sounds. Calls overlap, and the same animal may sound different depending on its age, emotional state, distance from a microphone or social situation. Recordings may continue for months or years, while researchers have only a fraction of them carefully labeled with the caller, recipient and behavior.
Machine learning helps make those recordings searchable. A simplified workflow looks like this:
- Capture sound. Microphones, hydrophones or animal-borne tags record changes in air or water pressure.
- Represent the signal. Researchers examine waveforms, spectrograms—which show how sound energy changes across frequency and time—or, for whale clicks, sequences of intervals between sounds.
- Detect and sort. Models find likely calls and group or classify them by type, species, individual or other labeled feature.
- Add context. Where available, researchers connect calls with video, known identities, movement, social relationships and observed behavior.
- Test a hypothesis. Scientists check whether a pattern predicts something meaningful, often by measuring how animals respond to playback.
Some models are trained on examples that people have labeled, a method called supervised learning. Others look for clusters without being given categories in advance, or learn useful representations by predicting missing or neighboring pieces of sound. These approaches can help with the large volumes of recordings for which labels are scarce. But a cluster is a lead to investigate, not proof that animals use that cluster as a word.
In one 2019 sperm-whale study, models detected clicks, classified coda types, distinguished vocal clans and identified individuals in the study data. The reported figures were task- and dataset-specific: 99.5% click-detection accuracy on 650 spectrograms, 97.5% classification accuracy for 23 coda types in one Dominica dataset, and 99.4% individual-identification accuracy for two whales. Those results show what pattern recognition can do under particular conditions; they do not establish universal performance or decode what a coda means. Read the study.
Elephants: the strongest recent case for non-imitative labels
The most direct recent evidence for animal calls that resemble names comes from a 2024 study of wild African savannah elephants. Researchers asked whether a call’s acoustic structure contains information about the individual receiving it.
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A machine-learning model used the call’s sound to predict its likely recipient. That is not the same as identifying the elephant that called: the question was, in effect, “Who was this call for?” Researchers then played calls back to elephants and compared their responses. The animals responded differently to calls originally addressed to themselves than to calls addressed to another elephant.
Together, the model’s predictions and the playback responses support the idea that elephants use individually specific, name-like calls. The calls apparently do not simply copy the recipient’s own vocalization, a distinction that makes the result especially interesting. Human names generally do not imitate the person being named; the elephant finding raises questions about how vocal labels may have evolved in different species.
There are limits to what the study shows. It does not give us an English translation for a call, prove that every elephant has one fixed sound-name, or demonstrate human-like vocabulary or grammar. Rather, it finds evidence that a call can carry information about its intended recipient and that elephants react to that information. Read the elephant study.
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Bottlenose dolphins are a useful comparison. Individuals develop distinctive “signature whistles,” which can function as identity signals. Research has found that dolphins can address another dolphin by copying that individual’s signature whistle.
That is name-like, but it differs from the elephant result. A dolphin may use an imitation of the addressee’s learned whistle; the elephant calls studied appear to convey receiver-specific information without simply imitating the recipient. These findings should not be collapsed into one universal naming system. Read a research briefing on the comparison and see the research on dolphin signature whistles.
Sperm whales: finding structure without a translation key
Sperm whales communicate socially using sequences of clicks called codas. Machine-learning tools have helped researchers detect and classify these sequences, identify individuals and distinguish vocal clans. Project CETI combines machine learning with robotics and field research to investigate sperm-whale communication.
A 2024 study analyzed 8,719 codas from the Eastern Caribbean and described four dimensions of their structure: rhythm, tempo, rubato (timing changes relative to neighboring codas) and ornamentation, such as added clicks or changes to a basic pattern. These features can combine to produce a larger range of distinguishable patterns than researchers had previously recognized. The paper calls this a “sperm whale phonetic alphabet,” but the phrase describes aspects of sound structure—not a list of translated meanings. The communicative functions of many patterns remain unresolved. Read the coda study.
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What newer audio models can—and cannot—do
Researchers are also building models intended to handle bioacoustic data more broadly. Earth Species Project’s NatureLM-audio is described as an audio-language foundation model for tasks including species classification, sound detection, individual or speaker diarization, counting and behavioral labeling. Foundation models may provide reusable tools that researchers can adapt when a species has relatively few labeled recordings. Read the NatureLM-audio paper and learn about Earth Species Project’s methods.
Reuse does not mean every model will work equally well for every animal. Species differ in anatomy, hearing, social life and the kinds of signals they produce. A model trained on one population may pick up local recording conditions or fail when used with a different species.
Google’s DolphinGemma, developed with Georgia Tech and the Wild Dolphin Project, is trained on long-running audio-video data linked to Atlantic spotted dolphins, including identities, life histories and observed behavior. It is designed to model dolphin vocal structure and generate dolphin-like sequences. Generating a plausible sequence is not the same as producing a meaningful utterance: a model can predict what sound is statistically likely next without knowing what a dolphin would understand. An output must be judged by dolphin behavior, not by how convincing it sounds to people. Read Google’s DolphinGemma announcement.
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How scientists move from a pattern to a claim about meaning
A useful way to assess claims about animal communication is to think of them as an evidence ladder:
- Acoustic regularity: A sound or sound pattern recurs.
- Classification: A model or human listener can distinguish it from other signals.
- Association: It correlates with a caller, recipient, context or behavior.
- Receiver sensitivity: The animal responds differently to the signal in a relevant way.
- Manipulation: Altering the sound changes the response predictably.
- Generalization: The result holds across animals, groups, locations and recording conditions.
- Interactive validation: Animals respond appropriately to signals selected or generated by a machine.
Playback experiments are particularly valuable because they test whether animals treat a signal as behaviorally relevant. The elephant study went beyond identifying acoustic patterns by testing the animals’ responses. More generally, a convincing result should guard against shortcuts: for example, a model might appear to identify a call type when it has actually learned which microphone, location, recording day or social group is associated with it.
Behavior also needs context. A call recorded during feeding might relate to food, the caller’s identity, a location or the presence of a companion. Video and movement data can help disentangle these possibilities, but sound may still be only one part of communication. Posture, orientation, touch, scent and distance can matter too.
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Humans naturally hear familiar patterns in unfamiliar sounds. A computer-generated call may sound purposeful to us while having no comparable meaning for the animal. Recognition and prediction are not translation: a model can learn which sounds tend to follow others without establishing what a receiver understands.
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Other obstacles include small or uneven datasets, variation between individuals, and recordings that capture some social settings but not others. A model may perform well when recordings from the same encounter or equipment setup appear in both training and test data, yet fail on new animals or in the wild. Communication may also depend on visual or environmental information absent from an audio file.
For these reasons, a claim that AI has “decoded animal language” should be treated cautiously. Current work more securely supports narrower statements: models can detect and classify animal sounds, recognize some individuals, uncover acoustic structure, and help test whether calls carry information about a recipient or context.
What this could mean for conservation—and for animals
Better acoustic analysis could help researchers monitor species, populations and social groups; detect changes in behavior; and study ecosystem soundscapes. The Earth Species Project identifies conservation and ecosystem monitoring as potential applications. These are research and conservation possibilities, not guaranteed results from a consumer app. Read the project’s overview.
There are real risks alongside those possibilities. Highly precise recordings could reveal where rare animals gather. Playback or other equipment may disturb animals. Poorly validated claims can encourage people to treat entertainment-oriented “pet translators” as reliable explanations of a pet’s thoughts. Researchers also need to consider the welfare of animals studied and whether local and Indigenous ecological knowledge is collected and used with consent.
A sensible principle is to listen before trying to speak: prioritize passive monitoring and careful behavioral tests, and do not broadcast machine-generated calls simply because they sound plausible to us. The near-term achievement is not an English dictionary for animals. It is a more rigorous way to discover patterns, ask better questions and test—through the animals themselves—what those patterns do.
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