No abstract

International audienceDepression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities ( audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features ( emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying notdepressed individuals on the development set and 0.52/0.81, respectively for the test set

Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation.

Listeners are exposed to different types of speech in everyday life, from natural speech to speech that has undergone modifications or has been generated synthetically. While many studies have focused on measuring the intelligibility of these distinct speech types, their impact on listening effort is not known. The current study combined an objective measure of intelligibility, a physiological measure of listening effort (pupil size) and listeners' subjective judgements, to examine the impact of four speech types: plain (natural) speech, speech produced in noise (Lombard speech), speech enhanced to promote intelligibility, and synthetic speech. For each speech type, listeners responded to sentences presented in one of three levels of speech-shaped noise. Subjective effort ratings and intelligibility scores showed an inverse ranking across speech types, with synthetic speech being the most demanding and enhanced speech the least. Pupil size measures indicated an increase in listening effort with decreasing signal-to-noise ratio for all speech types apart from synthetic speech, which required significantly more effort at the most favourable noise level. Naturally and artificially modified speech were less effortful than plain speech at the more adverse noise levels. These outcomes indicate a clear impact of speech type on the cognitive demands required for comprehension.

No abstract