NDLI: Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (AVEC '14)

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Automatic Assessment of Depression from Speech and Behavioural Signals

AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge

Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video

Fusing Affective Dimensions and Audio-Visual Features from Segmented Video for Depression Recognition: INAOE-BUAP's Participation at AVEC'14 Challenge

Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression

Ensemble CCA for Continuous Emotion Prediction

Model Fusion for Multimodal Depression Classification and Level Detection

Emotion Recognition and Depression Diagnosis by Acoustic and Visual Features: A Multimodal Approach

Building a Database of Political Speech: Does Culture Matter in Charisma Annotations?

Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

The SRI AVEC-2014 Evaluation System

Inferring Depression and Affect from Application Dependent Meta Knowledge

Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (AVEC '14)

Content Provider	ACM Digital Library
Editor	Epps, Julien Valstar, Michel Cowie, Roddy Krajewski, Jarek Pantic, Maja Schuller, Björn
Copyright Year	2014
Abstract	It is our great pleasure to welcome you to the 4th Audio-Visual Emotion Recognition Challenge (AVEC 2014), held in conjunction with the ACM Multimedia 2014. This year's challenge and associated workshop continues to push the boundaries of audio-visual emotion recognition. The first AVEC challenge posed the problem of detecting discrete emotion classes on an extremely large set of natural behaviour data. The second AVEC extended this problem to the prediction of continuous valued dimensional affect on the same set of challenging data. In its third edition, we extended the problem even further to include the prediction of self-reported severity of depression, which is a frequently occurring mood disorder. Finally, this year we've focussed the study of depression and affect by narrowing down the number of tasks to be used, and enriched the annotation by adding another dimension to the data and increasing the number of annotators. The mission of AVEC challenge and workshop series is to provide a common benchmark test set for individual multimodal information processing and to bring together the audio and video emotion recognition communities, to compare the relative merits of the two approaches to emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. A second motivation is the need to advance emotion recognition systems to be able to deal with naturalistic behaviour in large volumes of un-segmented, non-prototypical and non-preselected. As you will see, these goals have been reached with the selection of this year's data and the challenge contributions. The call for participation attracted 22 submissions from Asia, Europe, and North America. The programme committee accepted 8 papers in addition to the baseline paper for oral presentation, and 4 papers for poster presentation. For the affect sub-challenge, 24 results submissions were made by 7 teams, and for the depression sub-challenge no less than 44 submissions by 13 teams! We hope that these proceedings will serve as a valuable reference for researchers and developers in the area of audio-visual emotion recognition and depression analysis. We also encourage attendees to attend the keynote presentations. This valuable and insightful talk can and will guide us to a better understanding of the state of the field, and future directions: Automatic Assessment of Depression from Speech and Behavioural Signals, Dr Julien Epps (University of New South Wales).
ISBN	9781450331197
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Access Restriction	Subscribed
Content Type	Text
Resource Type	Conference Proceedings

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