Named one of the Top 21 Mental Health companies worldwide!

  • Named one of the Top 21 Mental Health companies worldwide!

  • Named one of the Top 21 Mental Health companies worldwide!

Publications

With over 200 publications between the team, thymia has proven scientific excellence.

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publications

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publications

Paper

22 June 2023

Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data

Our paper got accepted into interspeech 2023!

In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

Paper

22 June 2023

Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data

Our paper got accepted into interspeech 2023!

In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

Paper

30 March 2022

Speech and the n-Back task as a lens into depression

Our paper got accepted into interspeech 2022!

This paper presents strong evidence that embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD).

We then present a set of machine learning experiments that demonstrate that combining speech with features from an n-Back working memory assessment improves classifier performance when predicting the popular eight-item Patient Health Questionnaire depression scale (PHQ-8).

Paper

30 March 2022

Speech and the n-Back task as a lens into depression

Our paper got accepted into interspeech 2022!

This paper presents strong evidence that embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD).

We then present a set of machine learning experiments that demonstrate that combining speech with features from an n-Back working memory assessment improves classifier performance when predicting the popular eight-item Patient Health Questionnaire depression scale (PHQ-8).