A comparative study of different classifiers for detecting depression from spontaneous speech

Alghowinem, Sharifa; Goecke, Roland; Wagner, Michael; Epps, Julien; Gedeon, Tom; Breakspear, Michael; Parker, Gordon

doi:10.1109/icassp.2013.6639227

Cited by 86 publications

(49 citation statements)

References 20 publications

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“…In this context, the GMM serves as dimensionality reduction, as well as a hybrid classification method [2]. The Hidden Markov Model Toolkit (HTK) was used to implement a HMM using one state to train the GMM models.…”

Section: Classification and Evaluationmentioning

confidence: 99%

Exploring Eye Activity as an Indication of Emotional States Using an Eye-Tracking Sensor

Alghowinem

Alshehri

Goecke

et al. 2014

Intelligent Systems for Science and Information

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Abstract. The automatic detection of human emotional states has been of great interest lately for its applications not only in the Human-Computer Interaction field, but also for its applications in psychological studies. Using an emotion elicitation paradigm, we investigate whether eye activity holds discriminative power for detecting affective states. Our emotion elicitation paradigm includes induced emotions by watching emotional movie clips and spontaneous emotions elicited by interviewing participants about emotional events in their life. To reduce gender variability, the selected participants were 60 female native Arabic speakers (30 young adults, and 30 mature adults). In general, the automatic classification results using eye activity were reasonable, giving 66% correct recognition rate on average. Statistical measures show statistically significant differences in eye activity patterns between positive and negative emotions. We conclude that eye activity, including eye movement, pupil dilation and pupil invisibility could be used as a complementary cues for the automatic recognition of human emotional states.

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Section: Classification and Evaluationmentioning

confidence: 99%

Exploring Eye Activity as an Indication of Emotional States Using an Eye-Tracking Sensor

Alghowinem

Alshehri

Goecke

et al. 2014

Intelligent Systems for Science and Information

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“…For the hybrid classifier, we create a GMM model for each subject, then feed thse models including mean, variance and weights to the SVM classifier. The use of GMMs served as dimensionality reduction, as well as to get the same number of features to be fed to the SVM regardless of the subject's interview duration [20]. For the functional features, we use an SVM for classification.…”

Section: E Classification and Evaluationmentioning

confidence: 99%

Head Pose and Movement Analysis as an Indicator of Depression

Alghowinem

Goecke

Wagner

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Abstract-Depression is a common and disabling mental health disorder, which impacts not only on the sufferer but also their families, friends and the economy overall. Our ultimate aim is to develop an automatic objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. Here, we analyse the performance of head pose and movement features extracted from face videos using a 3D face model projected on a 2D Active Appearance Model (AAM). In a binary classification task (depressed vs. non-depressed), we modelled low-level and statistical functional features for an SVM classifier using real-world clinically validated data. Although the head pose and movement would be used as a complementary cue in detecting depression in practice, their recognition rate was impressive on its own, giving 71.2% on average, which illustrates that head pose and movement hold effective cues in diagnosing depression. When expressing positive and negative emotions, recognising depression using positive emotions was more accurate than using negative emotions. We conclude that positive emotions are expressed less in depressed subjects at all times, and that negative emotions have less discriminatory power than positive emotions in detecting depression. Analysing the functional features statistically illustrates several behaviour patterns for depressed subjects: (1) slower head movements, (2) less change of head position, (3) longer duration of looking to the right, (4) longer duration of looking down, which may indicate fatigue and eye contact avoidance. We conclude that head movements are significantly different between depressed patients and healthy subjects, and could be used as a complementary cue.

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“…When used to detect depression they found glottal features to differentiate between the 2 groups with 75% accuracy. Alghowinem et al (2013) investigated a number of feature sets for detecting depression from spontaneous speech and found loudness and intensity features to be the most discriminative.…”

Section: Speech Indicatorsmentioning

confidence: 99%

A Cross-modal Review of Indicators for Depression Detection Systems

Morales¹,

Scherer²,

Levitan³

2017

Proceedings of the Fourth Workshop on Computational Linguistics And Clinical Psychology –- From Linguistic Signal to

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Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.

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A comparative study of different classifiers for detecting depression from spontaneous speech

Cited by 86 publications

References 20 publications

Exploring Eye Activity as an Indication of Emotional States Using an Eye-Tracking Sensor

Exploring Eye Activity as an Indication of Emotional States Using an Eye-Tracking Sensor

Head Pose and Movement Analysis as an Indicator of Depression

A Cross-modal Review of Indicators for Depression Detection Systems

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