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Human speech contains a rich set of acoustic biomarkers. When properly leveraged, these biomarkers can give powerful insights into the physical and mental health of the speaker. By exploiting these vocal biomarkers, machine learning models can be trained to detect altered speech patterns caused by depression or other mental health disorders. These speech based models serve as powerful, accurate, and non-invasive diagnostic tools. Prior works have explored the potential of these models and proven the feasibility of such systems on toy datasets. To see if these models have potential as a medical device, I re-implement some of these works on a dataset two orders of magnitude larger. Additionally, I introduce a new model that dramatically outperforms the current standard of care. I end with an investigation into this model’s behaviour and a discussion of potentially relevant biomarkers.

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