Mitigating Confounding Factors in Depression Detection Using an Unsupervised Clustering Approach

This work focuses on using speech as an objective marker for depression detection. One of the major challenges of this task is the presence of confounding factors, such as gender, age, emotion and personality. This work presents a technique to mitigate such factors by using a multi-step approach that performs unsupervised clustering prior to depression classification.

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  W. Low,et al.  Beck Depression Inventory (BDI): a reliability and validity test in the Malaysian urological population. , 2001, The Medical journal of Malaysia.

[3]  Thomas F. Quatieri,et al.  On the relative importance of vocal source, system, and prosody in human depression , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[4]  Thomas F. Quatieri,et al.  Vocal-Source Biomarkers for Depression: A Link to Psychomotor Activity , 2012, INTERSPEECH.

[5]  Thomas F. Quatieri,et al.  Phonologically-based biomarkers for major depressive disorder , 2011, EURASIP J. Adv. Signal Process..

[6]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.

[7]  Vidhyasaharan Sethu,et al.  Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Douglas E. Sturim,et al.  Automatic Detection of Depression in Speech Using Gaussian Mixture Modeling with Factor Analysis , 2011, INTERSPEECH.

[9]  Antonio Nucci,et al.  Fuzzy-Clustering-Based Decision Tree Approach for Large Population Speaker Identification , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Björn W. Schuller,et al.  AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge , 2014, AVEC '14.

[11]  K. Scherer.  Vocal affect expression: a review and a model for future research. , 1986, Psychological bulletin.

[12]  Tomi Kinnunen.  COMPARISON OF CLUSTERING ALGORITHMS IN SPEAKER IDENTIFICATION , 2000 .

[13]  Roland Göcke,et al.  An Investigation of Depressed Speech Detection: Features and Normalization , 2011, INTERSPEECH.

[14]  Roland Göcke,et al.  Diagnosis of depression by behavioural signals: a multimodal approach , 2013, AVEC@ACM Multimedia.

[15]  Björn W. Schuller,et al.  AVEC 2013: the continuous audio/visual emotion and depression recognition challenge , 2013, AVEC@ACM Multimedia.

[16]  Björn W. Schuller,et al.  Recent developments in openSMILE, the munich open-source multimedia feature extractor , 2013, ACM Multimedia.