Mohammed Senoussaoui
ABSTRACT
Audio-visual emotion and mood disorder cues have been recently explored to develop tools to assist psychologists and psychiatrists in evaluating a patient's level of depression. In this paper, we present a number of different multimodal depression level predictors using a model fusion approach, in the context of the AVEC14 challenge. We show that an i-vector based representation for short term audio features contains useful information for depression classification and prediction. We also employed a classification step prior to regression to allow having different regression models depending on the presence or absence of depression. Our experiments show that a combination of our audio-based model and two other models based on the LGBP-TOP video features lead to an improvement of 4% over the baseline model proposed by the challenge organizers.
- A. T. Beck, R. A. Steer, R. Ball, and W. F. Ranieri. Comparison of beck depression inventories-ia and-ii in psychiatric outpatients. Journal of personality assessment, 67(3):588--597, 1996.Google Scholar
- N. Cummins, J. Epps, M. Breakspear, and R. Goecke. An investigation of depressed speech detection: Features and normalization. In INTERSPEECH, pages 2997--3000. ISCA, 2011.Google Scholar
- N. Cummins, J. Epps, V. Sethu, and J. Krajewski. Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 970--974, May 2014.Google ScholarCross Ref
- N. Cummins, J. Joshi, A. Dhall, V. Sethu, R. Goecke, and J. Epps. Diagnosis of depression by behavioural signals: A multimodal approach. In Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, AVEC '13, pages 11--20, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet. Front-end factor analysis for speaker verification. Audio, Speech, and Language Processing, IEEE Transactions on, 19(4):788--798, 2011. Google ScholarDigital Library
- N. Dehak, P. A. Torres-Carrasquillo, D. A. Reynolds, and R. Dehak. Language recognition via i-vectors and dimensionality reduction. In INTERSPEECH, pages 857--860, 2011.Google Scholar
- A. Dobson. An Introduction to Genelarized Linear Models. Chapman & Hall/CRC; 3 edition, 2008.Google Scholar
- H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines. Advances in neural information processing systems, 9:155--161, 1997.Google Scholar
- J. S. Garofolo, L. D. Consortium, et al. TIMIT: acoustic-phonetic continuous speech corpus, 1993.Google Scholar
- R. Horwitz, T. F. Quatieri, B. S. Helfer, B. Yu, J. R. Williamson, and J. Mundt. On the relative importance of vocal source, system, and prosody in human depression. In Body Sensor Networks (BSN), 2013 IEEE International Conference on, pages 1--6, May 2013.Google ScholarCross Ref
- P. Kenny. A small foot-print i-vector extractor. In Proc. Odyssey, 2012.Google Scholar
- P. Lopez-Otero, L. Docio-Fernandez, and C. Garcia-Mateo. A study of acoustic features for the classification of depressed speech. In Proceedings of the International Convention Mipro Conference On Intelligent Systems (CIS), Special Session on Biometrics & Forensics & De-identification and Privacy Protection (BiForD). MIPRO, May 2014.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- S. O. Sadjadi, M. Slaney, and L. Heck. Msr identity toolbox v1.0: A matlab toolbox for speaker-recognition research. Speech and Language Processing Technical Committee Newsletter, November 2013.Google Scholar
- M. Senoussaoui, P. Kenny, T. Stafylakis, and P. Dumouchel. A study of the cosine distance-based mean shift for telephone speech diarization. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(1):217--227, 2014. Google ScholarDigital Library
- S. Shum, N. Dehak, E. Chuangsuwanich, D. A. Reynolds, and J. R. Glass. Exploiting intra-conversation variability for speaker diarization. In INTERSPEECH, pages 945--948, 2011.Google Scholar
- 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. In Proceedings of Interspeech, 2011.Google Scholar
- E. M. Tipping. Sparse Bayesian learning and the relevance vector machine. Journal Machine Learning Research, 1:211--244, 2001. Google ScholarDigital Library
- M. Valstar, B. Schuller, K. Smith, T. Almaev, F. Eyben, J. Krajewski, R. Cowie, and M. Pantic. AVEC 2014 - 3d dimensional affect and depression recognition challenge. In Proceedings of the 4th International Audio/Visual Emotion Challenge and Workshop (to appear). SSPNET, November 2014. Google ScholarDigital Library
- V. Vapnik. Statistical Learning Theory. Wiley-Interscience, September 1998.Google Scholar
- J. R. Williamson, T. F. Quatieri, B. S. Helfer, R. Horwitz, B. Yu, and D. D. Mehta. Vocal biomarkers of depression based on motor incoordination. In Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, AVEC '13, pages Google ScholarDigital Library
Index Terms
- Model Fusion for Multimodal Depression Classification and Level Detection
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