Work2012

Speech analysis for mood state characterization in bipolar patients

Nicola Vanello · Andrea Guidi · Claudio Gentili · Gilles Bertschy · Gaetano Valenza · Antonio Lanatà +1 more

67 citations0/0 files storedAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Emotion and Mood Recognition Voice and Speech Disorders Bipolar Disorder and Treatment

Abstract

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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References

2009Praat script to detect syllable nuclei and measure speech rate automatically 2000Acoustical properties of speech as indicators of depression and suicidal risk 2006Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology 2004Voice acoustical measurement of the severity of major depression 2007Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech 2004Investigation of Vocal Jitter and Glottal Flow Spectrum as Possible Cues for Depression and Near-Term Suicidal Risk 1951USES OF THE THEMATIC APPERCEPTION TEST

Cited by

2014Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls 2020Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study 2022Detection of major depressive disorder, bipolar disorder, schizophrenia and generalized anxiety disorder using vocal acoustic analysis and machine learning: an exploratory study

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