You sound down: Using AI to spot depression in a person’s voice

depression
[T]he notion that artificial intelligence could help predict if a person is suffering from depression is potentially a big step forward—albeit one that brings with it questions about how it might be used.

What makes that possible, says Tuka Alhanai, a researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), is the ability of a machine learning model to identify speech and language patterns associated with depression. More importantly, the model she and fellow MIT scientist Mohammad Ghassemi developed was able to recognize depression with a relatively high degree of accuracy through analyzing how people speak, rather than their specific responses to a clinician’s questions.

It’s what Alhanai refers to as “context-free” analysis; in other words, the model takes its cues from the words people choose and how they say them, without trying to interpret the meaning of their statements.

Related article:  Deep learning: When artificial and human intelligence come together

The potential benefit, Alhanai notes, is that this type of neural network approach could one day be used to evaluate a person’s more natural conversations outside a formal, structured interview with a clinician. That could be helpful in encouraging people to seek professional help when they otherwise might not, due to cost, distance or simply a lack of awareness that something’s wrong.

Read full, original post: Can Artificial Intelligence Detect Depression in a Person’s Voice?

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