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Artificial intelligence could translate dog vocalisation
Researchers adapted a tool previously trained for human speech.

Technology can distinguish between playfulness and aggression.

Researchers from the University of Michigan are exploring how artificial intelligence (AI) could be used to decipher dog barks.

The AI model has the potential to discover information from animal vocalisations, including the dog’s age, breed and sex. The researchers also believe it could identify if a bark is playful or aggressive.

The project saw researchers adapt a speech-processing model, which was previously trained to study human speech.

Through a collaboration with the National Institute of Astrophysics, Optics and Electronics (INAOE) Institute in Mexico, the team discovered that this model could act as a starting point for training new systems for animal communication.

The development of an AI model for animal vocalisations was previously hampered by the lack of public data. Although human samples are easy to record, there are more limitations when collecting animal recordings.

Researchers say that animal vocalisations are logistically more difficult to record as they either need to be recorded in the wild or, for domestic pets, with the permission of owners.

It was due to these limitations that researchers opted to instead repurpose an existing, human-oriented model.

Existing voice technologies, such as voice-to-text and language translation, are trained to identify the nuances of human speech. The tools are able to distinguish between tone, pitch and accent to translate speech and identify speakers.

The team adapted this model by using a dataset of dog vocalisations from 74 different dogs – of varying breed, age, sex and context. These recordings were then used to modify the machine-learning model.

Using this tool, researchers were able to generate and interpret acoustic representations from the dogs. The AI model not only passed four different classification tasks, but also outperformed other models specifically trained on dog barks with accuracy figures of up to 70 per cent.

Rada Mihalcea, from the University of Michigan’s AI laboratory, said: "This is the first time that techniques optimised for human speech have been built upon to help with the decoding of animal communication.

"Our results show that the sounds and patterns derived from human speech can serve as a foundation for analysing and understanding the acoustic patterns of other sounds, such as animal vocalisations."

The full study can be found here.

Image © Shutterstock

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Free CPD announced for BVNA members

News Story 1
 Zoetis is to present a CPD event for free to members of the British Veterinary Nursing Association (BVNA).

Led by veterinary consultant Ruth Moxon, the one-hour online session is designed to help veterinary nurses discuss parasiticide options with clients. It will advise on structuring recommendations, factors for product choice and moving away from 'selling'.

'How do you recommend parasite treatments to your clients?' will be presented on Tuesday, 20 May at 7.30pm. It is free for BVNA members, with £15.00 tickets for non-members.

Veterinary nurses can email cpd@bvna.co.uk to book their place. 

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News Shorts
DAERA to reduce BVD 'grace period'

DAERA has reminded herd keepers of an upcoming reduction to the 'grace period' to avoid BVD herd restrictions.

From 1 May 2025, herd keepers will have seven days to cull any BVD positive or inconclusive animals to avoid restrictions being applied to their herd.

It follows legislation introduced on 1 February, as DAERA introduces herd movement restrictions through a phased approach. Herd keepers originally had 28 days to cull BVD positive or inconclusive animals.

DAERA says that, providing herd keepers use the seven-day grace period, no herds should be restricted within the first year of these measures.

Additional measures, which will target herds with animals over 30 days old that haven't been tested for BVD, will be introduced from 1 June 2025.

More information is available on the DAERA website.