A new AI tool can tell playful barks from aggressive ones—as well as identifying the dog’s age, sex, and breed.
A new study finds that AI models originally trained on human speech can be used as a starting point to train new systems that target animal communication.
“By using speech processing models initially trained on human speech, our research opens a new window into how we can leverage what we built so far in speech processing to start understanding the nuances of dog barks,” says Rada Mihalcea, professor of computer science and engineering and director of the AI Laboratory at the University of Michigan.
“There is so much we don’t yet know about the animals that share this world with us. Advances in AI can be used to revolutionize our understanding of animal communication, and our findings suggest that we may not have to start from scratch.”
One of the prevailing obstacles to developing AI models that can analyze a dog’s bark or other animal vocalizations is the lack of publicly available data. While there are numerous resources and opportunities for recording human speech, collecting such data from animals is more difficult.
“Animal vocalizations are logistically much harder to solicit and record,” says lead author Artem Abzaliev, a doctoral student in computer science and engineering. “They must be passively recorded in the wild or, in the case of domestic pets, with the permission of owners.”
Because of this dearth of usable data, techniques for analyzing dog vocalizations have proven difficult to develop, and the ones that do exist are limited by a lack of training material. The researchers overcame these challenges by repurposing an existing model that was originally designed to analyze human speech.
This approach enabled the researchers to tap into robust models that form the backbone of the various voice-enabled technologies we use today, including voice-to-text and language translation. These models are trained to distinguish nuances in human speech, like tone, pitch, and accent, and convert this information into a format that a computer can use to identify what words are being said, recognize the individual speaking, and more.
“These models are able to learn and encode the incredibly complex patterns of human language and speech,” Abzaliev says. “We wanted to see if we could leverage this ability to discern and interpret dog barks.”
The researchers used a dataset of dog vocalizations recorded from 74 dogs of varying breed, age, and sex, in a variety of contexts.
Humberto Pérez-Espinosa, a collaborator at Mexico’s National Institute of Astrophysics, Optics and Electronics (INAOE) Institute, led the team who collected the dataset. Abzaliev then used the recordings to modify a machine-learning model—a type of computer algorithm that identifies patterns in large data sets. The team chose a speech representation model called Wav2Vec2, which was originally trained on human speech data.
With this model, the researchers were able to generate representations of the acoustic data collected from the dogs and interpret these representations. They found that Wav2Vec2 not only succeeded at four classification tasks; it also outperformed other models trained specifically on dog bark data, with accuracy figures up to 70%.
“This is the first time that techniques optimized for human speech have been built upon to help with the decoding of animal communication,” Mihalcea says. “Our results show that the sounds and patterns derived from human speech can serve as a foundation for analyzing and understanding the acoustic patterns of other sounds, such as animal vocalizations.”
In addition to establishing human speech models as a useful tool in analyzing animal communication—which could benefit biologists, animal behaviorists, and more—this research has important implications for animal welfare.
Understanding the nuances of dog vocalizations could greatly improve how humans interpret and respond to the emotional and physical needs of dogs, thereby enhancing their care and preventing potentially dangerous situations, the researchers say.
The researchers presented their findings at the Joint International Conference on Computational Linguistics, Language Resources and Evaluation.
Source: Emily France for University of Michigan
