Acoustical Properties of Speech as Indicators of Depression and Suicidal Risk

By M. Landau. 2008

317 citations recorded by the source corpus.

Acoustical properties of speech have been shown to be related to mental states such as remission and depression. The objective of this project was to relate the energy in frequency bands with the severity of the mental state using the Beck Depression Inventory (BDI). Recorded speech was obtained from male and female subjects with mental states of remission, depression, and suicidal risk. These subjects had recorded automated and spontaneous speech samples. Multiple regression analysis was used to relate the independent energy band ratio variables with the dependent BDI scores, and thus allow the determination of equitable BDI scores for future patients. For the male group, the square of the 3rd energy band and the cross-product of the 2nd and 3rd energy band were prominent in both the reading and interviewed groups. Therefore the equation with the 2nd lowest Akaike Information Criterion (AIC) score was chosen for the reading male group, and the 1st lowest AIC score was chosen for the interviewed male group. For the female group, the square and cross-product of the 1st and 2nd energy bands were prominent in both the reading and interviewed groups. Therefore the 2nd lowest AIC score was chosen for the reading female group, and the 1st lowest AIC score was chosen for the interviewed female group. The clinician could thus determine the patient’s mood or state of mind by comparing the estimated BDI score with the ranges of total BDI scores: remitted 0 – 20, depressed 15 – 38, suicidal 38 – 46. Keywords: speech, mental states, power spectra, multiple regression, information theoretic criterion

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/da34f2fa75912f2c31515ebd1ca8cf040194f759

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In Cardiology and Cardiovascular Medicine

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Citations in the corpus

Databases, features and classifiers for speech emotion recognition: a review (2018, background): “Analysis of interviews between a doctor and a patient before and after medication was used in France et al. (2000).”
Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network (2017, background): “Furthermore, medical use of speech emotion recognition includes diagnostic tool for therapists [5].”
Emotion recognition from speech: a review (2012, background): “Medical doctors may use emotional contents of a patient’s speech as a diagnosing tool for various disorders (France et al. 2000).”
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012, background): “In addition, certain vocal expressions and prosodic cues have been associated with specific mental illnesses such as Obsessive Compulsive Disorder (Aigner et al. 2007), Tourette Syndrome (Calder et al. 2001) or depression (France et al. 2000).”
Emotional speech recognition: Resources, features, and methods (2006, methodology): “It has been found that subjects during stress or under depression do not articulate voiced sounds with the same effort as in the neutral emotional state (Tolkmitt and Scherer, 1986; France et al., 2000).”
A review of depression and suicide risk assessment using speech analysis (2015, background): “, 2007, 2006), although there is some disagreement as to whether or not this is a relative shift in energy from lower to higher frequencies (France et al., 2000; Ozdas et al., 2004a) or vice versa (Yingthawornsuk and Shiavi, 2008; Yingthawornsuk et al.”
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones (2014, background): “There is a growing interest in using smartphone sensing [31, 9, 12, 11, 40, 8, 15] to infer human dynamics and behavioral health [7, 34, 25, 21, 18, 23, 35, 28, 30].”