As part of the call for “Development of artificial intelligence use cases applied to industry,” and in response to the growing need for more precise and automatic methods for emotional state evaluation, eB2 proposes a project focused on developing advanced algorithms for the automatic extraction of cognitive and emotional state indicators based on audio. This approach allows for passive and non-intrusive monitoring, facilitating the collection of reliable and accurate data. This, in turn, supports professionals in making decisions regarding interventions and preventive care.

Specifically, this project will enable eB2 to incorporate a new set of voice-based indicators not currently available in eB2’s indicator portfolio. Thanks to this support, our platform will identify subtle changes in tone, rhythm, and other speech elements that can indicate mental health issues such as depression or apathy, thus optimizing the monitoring process, improving therapeutic response, and offering a new differentiating feature to our clients.