Abstract
Epidemiological studies suggest that bipolar disorder has a prevalence of about 1% in European countries, becoming one of the most disabling illnesses in working age adults, and often long-term and persistent with complex management and treatment. Therefore, the capacity of home monitoring for patients with this disorder is crucial for their quality of life. The current paper introduces the use of speech-based information as an easy-to-record, ubiquitous and non-intrusive health sensor suitable for home monitoring, and its application in the framework on the NYMPHA-MD project. Some preliminary results also show the potential of acoustic and prosodic features to detect and classify bipolar disorder, by predicting the values of the Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS) from speech.
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