Speech analysis for mood state characterization in bipolar patients

Speech analysis for mood state characterization in bipolar patients

“Bipolar disorders are characterized by an unpredictable behavior, resulting in depressive, hypomanic or manic episodes alternating with euthymic states. A multi-parametric approach can be followed to estimate mood states by integrating information coming from different physiological signals and from the analysis of voice. In this work we propose an algorithm to estimate speech features from running speech with the aim of characterizing the mood state in bipolar patients. This algorithm is based on an automatic segmentation of speech signals to detect voiced segments, and on a spectral matching approach to estimate pitch and pitch changes. In particular average pitch, jitter and pitch standard deviation within each voiced segment, are estimated. The performances of the algorithm are evaluated on a speech database, which includes an electroglottographic signal. A preliminary analysis on subjects affected by bipolar disorders is performed and results are discussed.”

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Research Paper

Vanello, N., Guidi, A., Gentili, C., Werner, S., Bertschy, G., Valenza, G., … & Scilingo, E. P. (2012, August). Speech analysis for mood state characterization in bipolar patients. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 2104-2107). IEEE.

Read more about Bipolar Disorder [NIMH]


About the Author

A biomedical engineer by training, Jim Schwoebel was educated at Georgia Tech. He is currently Co-Founder and Partner in NeuroLaunch, the world's first neuroscience accelerator, and Founder/CEO of NeuroLex.

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