Abstract
Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, , and its changes. -related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.
Highlights
Mood disorders, especially bipolar disorders, have a great impact on people’s lives, and currently, large efforts are being made both to determine their causes and to improve therapy [1,2,3]
We explore the possibility of using a smartphone to collect and process speech data for the estimation of features related to the speech F0 and its variability measured over voiced segments
The results indicate a correlation between mood state changes and median absolute deviation (MAD) of the distribution of the meanF0, indicated as M AD_meanF0 (ρ = 0.54, p-value = 0.0392)
Summary
Especially bipolar disorders, have a great impact on people’s lives, and currently, large efforts are being made both to determine their causes and to improve therapy [1,2,3]. Manic periods (or hypomanic when the intensity of the symptoms are less severe) instead are marked by inflated self-esteem or grandiosity, increased energy, decreased need for sleep, increased talkativity, subjective experience that thoughts are racing or flying away, psychomotor agitation and increased risk-taking behaviors. Speech production is a complex phenomenon that is influenced by the autonomic and somatic nervous systems, through the modulation of breathing activity, vocal muscles tension, salivation and mucus secretion [11,12,13]. This observation led research towards the exploration of correlations between mood state and voice or speech signals in mood disorders. Prosodic and vocal tract features could discriminate depressed subjects with respect to controls [16]
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