Workgreen2010

Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents

Lu-Shih Alex Low · Namunu Chinthaka Maddage · Margaret Lech · Lisa Sheeber · Nicholas Brian Allen

78 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

In this paper, we report the influence that classification accuracies have in speech analysis from a clinical dataset by adding acoustic low-level descriptors (LLD) belonging to prosodic (i.e. pitch, formants, energy, jitter, shimmer) and spectral features (i.e. spectral flux, centroid, entropy and roll-off) along with their delta (Δ) and delta-delta (Δ-Δ) coefficients to two baseline features of Mel frequency cepstral coefficients and Teager energy critical-band based autocorrelation envelope. Extracted acoustic low-level descriptors (LLD) that display an increase in accuracy after being added to these baseline features were finally modeled together using Gaussian mixture models and tested. A clinical data set of speech from 139 adolescents, including 68 (49 girls and 19 boys) diagnosed as clinically depressed, was used in the classification experiments. For male subjects, the combination of (TEO-CB-Auto-Env + Δ + Δ-Δ) + F0 + (LogE + Δ + Δ-Δ) + (Shimmer + Δ) + Spectral Flux + Spectral Roll-off gave the highest classification rate of 77.82% while for the female subjects, using TEO-CB-Auto-Env gave an accuracy of 74.74%.

Citations in this library

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References

2000Acoustical properties of speech as indicators of depression and suicidal risk 2001Nonlinear feature based classification of speech under stress 1980Some observations on oral air flow during phonation 2007Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech 1996Vocal indicators of mood change in depression

Cited by

2018A deep architecture for depression detection using posting, behavior, and living environment data 2018Tracking depression severity from audio and video based on speech articulatory coordination 2018Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model 2023Bio-acoustic features of depression: A review 2015Multimodal depression recognition with dynamic visual and audio cues 2011Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis

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