AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661811

Linlin Chao, J. Tao, Minghao Yang, Ya Li, Zhengqi Wen

引用次数: 41

Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression 基于面部表情动态和回归的抑郁量表自动预测

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661812

Asim Jan, H. Meng, Y. F. A. Gaus, Fan Zhang, Saeed Turabzadeh

{"title":"Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression","authors":"Asim Jan, H. Meng, Y. F. A. Gaus, Fan Zhang, Saeed Turabzadeh","doi":"10.1145/2661806.2661812","DOIUrl":"https://doi.org/10.1145/2661806.2661812","url":null,"abstract":"Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behavior, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.","PeriodicalId":318508,"journal":{"name":"AVEC '14","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116909684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 80

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions 人机交互中情感维度与抑郁的多模态预测

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661810

Rahul Gupta, Nikos Malandrakis, Bo Xiao, T. Guha, Maarten Van Segbroeck, M. Black, A. Potamianos, Shrikanth S. Narayanan

{"title":"Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions","authors":"Rahul Gupta, Nikos Malandrakis, Bo Xiao, T. Guha, Maarten Van Segbroeck, M. Black, A. Potamianos, Shrikanth S. Narayanan","doi":"10.1145/2661806.2661810","DOIUrl":"https://doi.org/10.1145/2661806.2661810","url":null,"abstract":"Depression is one of the most common mood disorders. Technology has the potential to assist in screening and treating people with depression by robustly modeling and tracking the complex behavioral cues associated with the disorder (e.g., speech, language, facial expressions, head movement, body language). Similarly, robust affect recognition is another challenge which stands to benefit from modeling such cues. The Audio/Visual Emotion Challenge (AVEC) aims toward understanding the two phenomena and modeling their correlation with observable cues across several modalities. In this paper, we use multimodal signal processing methodologies to address the two problems using data from human-computer interactions. We develop separate systems for predicting depression levels and affective dimensions, experimenting with several methods for combining the multimodal information. The proposed depression prediction system uses a feature selection approach based on audio, visual, and linguistic cues to predict depression scores for each session. Similarly, we use multiple systems trained on audio and visual cues to predict the affective dimensions in continuous-time. Our affect recognition system accounts for context during the frame-wise inference and performs a linear fusion of outcomes from the audio-visual systems. For both problems, our proposed systems outperform the video-feature based baseline systems. As part of this work, we analyze the role played by each modality in predicting the target variable and provide analytical insights.","PeriodicalId":318508,"journal":{"name":"AVEC '14","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114673047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 94

Building a Database of Political Speech: Does Culture Matter in Charisma Annotations? 政治演讲数据库的构建:文化在魅力注释中起作用吗?

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661808

Ailbhe Cullen, Andrew Hines, N. Harte

引用次数: 8

AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge AVEC 2014: 3D维度影响和抑郁症识别挑战

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661807

M. Valstar, Björn Schuller, Kirsty Smith, Timur R. Almaev, F. Eyben, J. Krajewski, R. Cowie, M. Pantic

{"title":"AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge","authors":"M. Valstar, Björn Schuller, Kirsty Smith, Timur R. Almaev, F. Eyben, J. Krajewski, R. Cowie, M. Pantic","doi":"10.1145/2661806.2661807","DOIUrl":"https://doi.org/10.1145/2661806.2661807","url":null,"abstract":"Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence, arousal and dominance. In addition to structured self-report questionnaires, psychologists and psychiatrists use in their evaluation of a patient's level of depression the observation of facial expressions and vocal cues. It is in this context that we present the fourth Audio-Visual Emotion recognition Challenge (AVEC 2014). This edition of the challenge uses a subset of the tasks used in a previous challenge, allowing for more focussed studies. In addition, labels for a third dimension (Dominance) have been added and the number of annotators per clip has been increased to a minimum of three, with most clips annotated by 5. The challenge has two goals logically organised as sub-challenges: the first is to predict the continuous values of the affective dimensions valence, arousal and dominance at each moment in time. The second is to predict the value of a single self-reported severity of depression indicator for each recording in the dataset. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.","PeriodicalId":318508,"journal":{"name":"AVEC '14","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128667795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 354

The SRI AVEC-2014 Evaluation System SRI AVEC-2014评估体系

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661818

V. Mitra, Elizabeth Shriberg, Mitchell McLaren, A. Kathol, Colleen Richey, D. Vergyri, M. Graciarena

引用次数: 53

Model Fusion for Multimodal Depression Classification and Level Detection 多模态抑郁分类与水平检测的模型融合

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661819

Mohammed Senoussaoui, Milton Orlando Sarria Paja, J. F. Santos, T. Falk

引用次数: 55

Fusing Affective Dimensions and Audio-Visual Features from Segmented Video for Depression Recognition: INAOE-BUAP's Participation at AVEC'14 Challenge 从分割视频中融合情感维度和视听特征用于抑郁症识别:INAOE-BUAP参加AVEC'14挑战赛

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661815

Humberto Pérez Espinosa, H. Escalante, Luis Villaseñor-Pineda, M. Montes-y-Gómez, David Pinto, Verónica Reyes-Meza

{"title":"Fusing Affective Dimensions and Audio-Visual Features from Segmented Video for Depression Recognition: INAOE-BUAP's Participation at AVEC'14 Challenge","authors":"Humberto Pérez Espinosa, H. Escalante, Luis Villaseñor-Pineda, M. Montes-y-Gómez, David Pinto, Verónica Reyes-Meza","doi":"10.1145/2661806.2661815","DOIUrl":"https://doi.org/10.1145/2661806.2661815","url":null,"abstract":"Depression is a disease that affects a considerable portion of the world population. Severe cases of depression interfere with the common live of patients, for those patients a strict monitoring is necessary in order to control the progress of the disease and to prevent undesired side effects. A way to keep track of patients with depression is by means of online monitoring via human-computer-interaction. The AVEC'14 challenge aims at developing technology towards the online monitoring of depression patients. This paper describes an approach to depression recognition from audiovisual information in the context of the AVEC'14 challenge. The proposed method relies on an effective voice segmentation procedure, followed by segment-level feature extraction and aggregation. Finally, a meta-model is trained to fuse mono-modal information. The main novel features of our proposal are that (1) we use affective dimensions for building depression recognition models; (2) we extract visual information from voice and silence segments separately; (3) we consolidate features and use a meta-model for fusion. The proposed methodology is evaluated, experimental results reveal the method is competitive.","PeriodicalId":318508,"journal":{"name":"AVEC '14","volume":"802 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124173358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing 基于运动不协调和时间的抑郁的声音和面部生物标志物

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661809

J. Williamson, T. Quatieri, Brian S. Helfer, G. Ciccarelli, D. Mehta

{"title":"Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing","authors":"J. Williamson, T. Quatieri, Brian S. Helfer, G. Ciccarelli, D. Mehta","doi":"10.1145/2661806.2661809","DOIUrl":"https://doi.org/10.1145/2661806.2661809","url":null,"abstract":"In individuals with major depressive disorder, neurophysiological changes often alter motor control and thus affect the mechanisms controlling speech production and facial expression. These changes are typically associated with psychomotor retardation, a condition marked by slowed neuromotor output that is behaviorally manifested as altered coordination and timing across multiple motor-based properties. Changes in motor outputs can be inferred from vocal acoustics and facial movements as individuals speak. We derive novel multi-scale correlation structure and timing feature sets from audio-based vocal features and video-based facial action units from recordings provided by the 4th International Audio/Video Emotion Challenge (AVEC). The feature sets enable detection of changes in coordination, movement, and timing of vocal and facial gestures that are potentially symptomatic of depression. Combining complementary features in Gaussian mixture model and extreme learning machine classifiers, our multivariate regression scheme predicts Beck depression inventory ratings on the AVEC test set with a root-mean-square error of 8.12 and mean absolute error of 6.31. Future work calls for continued study into detection of neurological disorders based on altered coordination and timing across audio and video modalities.","PeriodicalId":318508,"journal":{"name":"AVEC '14","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126386833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 178

Inferring Depression and Affect from Application Dependent Meta Knowledge 从应用依赖元知识推断抑郁和影响

AVEC '14 Pub Date : 2014-11-07 DOI: 10.1145/2661806.2661813

Markus Kächele, Martin Schels, F. Schwenker

引用次数: 51

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