2015 International Conference on Affective Computing and Intelligent Interaction (ACII)最新文献_第10页

Cognitive state measurement from eye gaze analysis in an intelligent virtual reality driving system for autism intervention 自闭症干预智能虚拟现实驾驶系统中眼注视分析的认知状态测量

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344621

Lian Zhang, Joshua W. Wade, A. Swanson, A. Weitlauf, Z. Warren, N. Sarkar

{"title":"Cognitive state measurement from eye gaze analysis in an intelligent virtual reality driving system for autism intervention","authors":"Lian Zhang, Joshua W. Wade, A. Swanson, A. Weitlauf, Z. Warren, N. Sarkar","doi":"10.1109/ACII.2015.7344621","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344621","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities with a high prevalence rate. While much research has focused on improving social communication deficits in ASD populations, less emphasis has been devoted to improving skills relevant for adult independent living, such as driving. In this paper, a novel virtual reality (VR)-based driving system with different difficulty levels of tasks is presented to train and improve driving skills of teenagers with ASD. The goal of this paper is to measure the cognitive load experienced by an individual with ASD while he is driving in the VR-based driving system. Several eye gaze features are identified that varied with cognitive load in an experiment participated by 12 teenagers with ASD. Several machine learning methods were compared and the ability of these methods to accurately measure cognitive load was validated with respect to the subjective rating of a therapist. Results will be used to build models in an intelligent VR-based driving system that can sense a participant's real-time cognitive load and offer driving tasks at an appropriate difficulty level in order to maximize the participant's long-term performance.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"51 1","pages":"532-538"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79395135","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: 12

From simulated speech to natural speech, what are the robust features for emotion recognition? 从模拟语音到自然语音，情感识别的鲁棒性特征是什么?

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344597

Ya Li, Linlin Chao, Yazhu Liu, Wei Bao, J. Tao

{"title":"From simulated speech to natural speech, what are the robust features for emotion recognition?","authors":"Ya Li, Linlin Chao, Yazhu Liu, Wei Bao, J. Tao","doi":"10.1109/ACII.2015.7344597","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344597","url":null,"abstract":"The earliest research on emotion recognition starts with simulated/acted stereotypical emotional corpus, and then extends to elicited corpus. Recently, the demanding for real application forces the research shift to natural and spontaneous corpus. Previous research shows that accuracies of emotion recognition are gradual decline from simulated speech, to elicited and totally natural speech. This paper aims to investigate the effects of the common utilized spectral, prosody and voice quality features in emotion recognition with the three types of corpus, and finds out the robust feature for emotion recognition with natural speech. Emotion recognition by several common machine learning methods are carried out and thoroughly compared. Three feature selection methods are performed to find the robust features. The results on six common used corpora confirm that recognition accuracies decrease when the corpus changing from simulated to natural corpus. In addition, prosody and voice quality features are robust for emotion recognition on simulated corpus, while spectral feature is robust in elicited and natural corpus.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"64 1","pages":"368-373"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79580455","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: 19

Chinese microblog sentiment classification based on convolution neural network with content extension method 基于内容扩展卷积神经网络的中文微博情感分类

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344603

Xiao Sun, Fei Gao, Chengcheng Li, F. Ren

{"title":"Chinese microblog sentiment classification based on convolution neural network with content extension method","authors":"Xiao Sun, Fei Gao, Chengcheng Li, F. Ren","doi":"10.1109/ACII.2015.7344603","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344603","url":null,"abstract":"Related research for sentiment analysis on Chinese microblog is aiming at analyzing the emotion of posters. This paper presents a content extension method that combines post with its' comments into a microblog conversation for sentiment analysis. A new convolutional auto encoder which can extract contextual sentiment information from microblog conversation of the post is proposed. Furthermore, a DBN model, which is composed by several layers of RBM(Restricted Boltzmann Machine) stacked together, is implemented to extract some higher level feature for short text of a post. These RBM layers can encoder observed short text to learn hidden structures or semantics information for better feature representation. A ClassRBM (Classification RBM) layer, which is stacked on top of RBM layers, is adapted to achieve the final sentiment classification. The experiment results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than state-of-the-art surface learning models such as SVM or NB, which also proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"38 1","pages":"408-414"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78422426","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: 9

Gestural and Postural Reactions to Stressful Event: Design of a Haptic Stressful Stimulus 应激事件的手势和姿势反应:触觉应激刺激的设计

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344696

Yoren Gaffary, David Antonio Gómez Jáuregui, Jean-Claude Martin, M. Ammi

引用次数: 3

Framework for combination aware AU intensity recognition 组合感知AU强度识别框架

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344631

Isabel Gonzalez, W. Verhelst, Meshia Cédric Oveneke, H. Sahli, D. Jiang

引用次数: 3

Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues 将情感分析应用于面对面的人机交互:从检测到评估问题

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344545

Caroline Langlet, C. Clavel

引用次数: 9

A linear regression model to detect user emotion for touch input interactive systems 基于线性回归模型的触摸输入交互系统用户情感检测

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344693

S. Bhattacharya

引用次数: 7

Detection of negative emotions in speech signals using bags-of-audio-words 利用音频-词袋检测语音信号中的消极情绪

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344678

Florian B. Pokorny, F. Graf, F. Pernkopf, Björn Schuller

{"title":"Detection of negative emotions in speech signals using bags-of-audio-words","authors":"Florian B. Pokorny, F. Graf, F. Pernkopf, Björn Schuller","doi":"10.1109/ACII.2015.7344678","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344678","url":null,"abstract":"Boosted by a wide potential application spectrum, emotional speech recognition, i.e., the automatic computer-aided identification of human emotional states based on speech signals, currently describes a popular field of research. However, a variety of studies especially concentrating on the recognition of negative emotions often neglected the specific requirements of real-world scenarios, for example, robustness, real-time capability, and realistic speech corpora. Motivated by these facts, a robust, low-complex classification system for the detection of negative emotions in speech signals was implemented on the basis of a spontaneous, strongly emotionally colored speech corpus. Therefore, an innovative approach in the field of emotion recognition was applied as the core of the system - the bag-of-words approach that is originally known from text and image document retrieval applications. Thorough performance evaluations were carried out and a promising recognition accuracy of 65.6 % for the 2-class paradigm negative versus non-negative emotional states attests to the potential of bags-of-words in speech emotion recognition in the wild.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"4 1","pages":"879-884"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88698604","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: 35

Superframe segmentation based on content-motion correspondence for social video summarization 基于内容-运动对应的超帧分割用于社交视频摘要

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344674

Tao Zhuo, Peng Zhang, Kangli Chen, Yanning Zhang

引用次数: 1

Building autonomous sensitive artificial listeners (Extended abstract) 构建自主敏感的人工听者(扩展摘要)

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI: 10.1109/ACII.2015.7344610

M. Schröder, Elisabetta Bevacqua, R. Cowie, F. Eyben, H. Gunes, D. Heylen, M. Maat, G. McKeown, Sathish Pammi, M. Pantic, C. Pelachaud, Björn Schuller, E. D. Sevin, M. Valstar, M. Wöllmer

{"title":"Building autonomous sensitive artificial listeners (Extended abstract)","authors":"M. Schröder, Elisabetta Bevacqua, R. Cowie, F. Eyben, H. Gunes, D. Heylen, M. Maat, G. McKeown, Sathish Pammi, M. Pantic, C. Pelachaud, Björn Schuller, E. D. Sevin, M. Valstar, M. Wöllmer","doi":"10.1109/ACII.2015.7344610","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344610","url":null,"abstract":"This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and non-verbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and non-verbal behaviours required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and non-verbal behaviour, since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on non-verbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling etc. We first summarise three prototype versions of the SAL scenario, in which the behaviour of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analysing and synthesising the respective behaviours. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behaviour, dialogue management, and synthesis of speaker and listener behaviour of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse, and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"282 1","pages":"456-462"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88057551","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: 2