Vocal biomarkers of depression based on motor incoordination

AVEC 2013 - Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge

Volumn , Issue , 2013, Pages 41-47

  Williamson, James R ^a   Horwitz, Rachelle ^a   Quatieri, Thomas F ^a   Yu, Bea ^a   Helfer, Brian S ^a   Mehta, Daryush D ^a  

^a MIT LINCOLN LABORATORY   (United States)

Author keywords

Clinical assessment; Correlation structure; Gaussian mixture models; Incoordination; Major depressive disorder; Motor control; Vocal biomarkers

Indexed keywords

COMMUNICATION CHANNELS (INFORMATION THEORY); REGRESSION ANALYSIS;

CLINICAL ASSESSMENTS; CORRELATION STRUCTURE; GAUSSIAN MIXTURE MODEL; INCOORDINATION; MAJOR DEPRESSIVE DISORDER; MOTOR CONTROL;

OBJECT RECOGNITION;

EID: 84887444712     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2512530.2512531     Document Type: Conference Paper

References (28)

1. C. Sabin, and C. Sackeim, H.A. Psychomotor Symptoms of Depression. Am J Psychiatry, 154:4-17, 1997
2. J.S. Buyukdura, S.M. McClintock, P.E. Psychomotor retardation in depression: biological underpinnings, measurement, and treatment. Prog Neuropsychopharmacol Biol Psychiatry. 28(2):395-409, 2011.
3. J. K. Darby, N. Simmons, and P. A. Bergcr. Speech and voice parameters of depression: a pilot study. Journal of Communication Disorders, 11, 75 85, 1984.
4. A. J. Flint, S. E. Black, I. Campbell-Taylor, G. F. Gailey, and C. Levinton. Abnormal speech articulation, psychomotor retardation, and subcortical dysfunction in major depression. Journal of Psychiatric Research, 27(3):309-319, 1993.
5. M. Fava, K. Kendler. Major depressive disorder. Neuron 28(2), 335-341, 2000.
6. J. Mundt, P. Snyder, M. S. Cannizaro, K. Chappie, and D. S. Geralts. Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology. J. Neurolinguistics, 20(1): 50-64, 2007.
7. D. France, R. Shiavi, et al. Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Transactions on Biomedical Engineering, 47(7): 829, 2000.
8. T. F. Quatieri and N. Malyska. Vocal-Source Biomarkers for Depression: A Link to Psychomotor Activity. Interspeech, 2012.
9. L. A. Low, T. Maddage, M. Lech, L. Sheeber, and N. Allen. Influence of acoustic low-level descriptors in the detection of clinical depression in adults. Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010.
10. E. Moore II, M. Clements, J. Peifer, and L. Weisser. Analysis of prosodic variation in speech for clinical depression. Proceedings of the 25th Annual International Conference of the IEEE EMBS, 2003.
11. A. Ozdas, R. Shiavi, S. Silverman, M. Silverman, and D. Mitchell. Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Transactions on Biomedical Engineering, 51(9), 2004.
12. A. Trevino, T. F. Quatieri, and N. Malyska., Phonologically-Based Biomarkers for Major Depressive Disorder. EURASIP Journal on Advances in Signal Processing: Special Issue on Emotion and Mental State Recognition from Speech, 2011(1), 1-18, 2011.
13. D. Sturim, P. Torres-Carrasquillo, T. F. Quatieri, N. Malyska, and A. McCree. Automatic Detection of Depression in Speech using Gaussian Mixture Modeling with Factor Analysis. Interspeech, 2011.
14. B. S. Helfer, T. F. Quatieri, J. R. Williamson, D. D. Mehta, R. Horwitz, and B. Yu. Classification of depression state based on articulatory precision. Interspeech, 2013.
15. E. Tognoli and J. A. Scott Kelso. Brain coordination dynamics: True and false faces of phase synchrony and metastability. Prog Neurobiol. 87(1): 31-40, 2009.
16. M. D. Greicius, B. H. Flores, V. Menon, G. H. Glover, H. B. Solvason, H. Kenna, A. L. Reiss, and A. F. Schatzberg. Resting- State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biol Psychiatry, 62(5): 429-437, 2007.
17. S. L. Bressler, E. Tognoli, Operational principles of neurocognitive networks. International Journal of Psychophysiology 60:139-148, 2006.
18. M. Valstar, B. Schuller, K. Smith, et al. AVEC 2013 - The Continuous Audio/Visual Emotion and Depression Recognition Challenge. AVEC 2013.
19. R. Horwitz, T. F. Quatieri, B. Helfer, B. Yu, J. R. Williamson, and J. Mundt. On the Relative Importance of Vocal Source, System, and Prosody in Human Depression. IEEE Body Sensor Network Conference, Cambridge, MA, May 2013.
20. D. Rudoy, D. N. Spendley, and P. Wolfe. Conditionally linear Gaussian models for estimating vocal tract resonances, Proc. Interspeech, 526-529, 2007.
21. D. Mehta, D. Rudoy, and P. Wolfe. Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking. The Journal of the Acoustical Society of America, 132(3), 1732-1746, 2012.
22. J. R. Williamson, D. W. Bliss, and D. W. Browne. Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on (pp. 665-668). IEEE, 2011.
23. J. R. Williamson, D. W. Bliss, D. W. Browne, and J. T. Narayanan. Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy & Behavior, 25(2), 230-238, 2012.
24. Y. Pan, C. Guan, K. K. Ang, K. S. Phua, H. Yang, D. Huang, and S. H. Lim. Seizure detection based on spatiotemporal correlation and frequency regularity of scalp EEG. In Neural Networks (IJCNN), The 2012 International Joint Conference on (pp. 1-7). IEEE, 2012.
25. J. R. Williamson, D. W. Bliss, D. W. Browne, P. Indic, P., E. Bloch- Salisbury, and D. Paydarfar. Using physiological signals to predict apnea in preterm infants. In Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on (pp. 1098-1102). IEEE, 2011.
26. J. R. Williamson, D. W. Bliss, D. W. Browne, P. Indic, E. Bloch- Salisbury, and D. Paydarfar. Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals. IEEE Body Sensor Networks, 2013.
27. D. A. Reynolds, T. F. Quatieri, and R. B. Dunn. Speaker verification using adapted Gaussian mixture models. Digital signal processing, 10(1), 19-41, 2000.
28. J. F. Cohn, T. S. Kruez, I. Matthews, Y. Yang, M. H. Nguyen, M. Padilla,. and F. De la Torre. Detecting depression from facial actions and vocal prosody. In Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on (pp. 1-7). IEEE, 2009.