​MIT create AI which can detect depression from speech

MIT researcher's new AI uses neural networks to identify patterns in speech to determine whether an individual is depressed


MIT researchers have announced the creation of a new AI model which they have claimed is capable of detecting the markers of depression in individuals.

By studying speech patterns, word choices and writing styles, the AI can allegedly identify depression without the need for pointed, specific questions.

The model is being referred to as "context-free" because the AI analyzes how things are being communicated, rather than what is being communicated.

"The first hints we have that a person is happy, excited, sad, or has some serious cognitive condition, such as depression, is through their speech," commented Tuka Alhanai, the project's lead researcher. "If you want to deploy [depression-detection] models in scalable way you want to minimize the amount of constraints you have on the data you're using.

"You want to deploy it in any regular conversation and have the model pick up, from the natural interaction, the state of the individual," he added.

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In the tests run so far, the AI has had a success rate of 77% and has outperformed other models which relied more heavily on the "question and answer" structure. Due to the flexibility of the model, James Glass, a co-researcher on the project, thinks it may even eventually be used in mobile apps to help monitor for signs of distress in picked up speech and send alerts to doctors if necessary.

The team are looking to expand their model's capabilities by including additional data from patients with other cognitive disorders. Alhanai said of the model's potential: "It's not so much detecting depression, but it's a similar concept of evaluating, from an everyday signal in speech, if someone has cognitive impairment or not."

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