Our community has long pursued principles and methods for enabling fluid and effortless collaborations between people and computing systems. Forging deep connections between people and machines has come into focus over the last 25 years as a grand challenge at the intersection of artificial intelligence, human-computer interaction, and cognitive psychology. I will review experiences and directions with leveraging advances in perception, learning, and reasoning in pursuit of our shared dreams.
{"title":"Connections: 2015 ICMI Sustained Accomplishment Award Lecture","authors":"E. Horvitz","doi":"10.1145/2818346.2835500","DOIUrl":"https://doi.org/10.1145/2818346.2835500","url":null,"abstract":"Our community has long pursued principles and methods for enabling fluid and effortless collaborations between people and computing systems. Forging deep connections between people and machines has come into focus over the last 25 years as a grand challenge at the intersection of artificial intelligence, human-computer interaction, and cognitive psychology. I will review experiences and directions with leveraging advances in perception, learning, and reasoning in pursuit of our shared dreams.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89114812","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}
This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant's nonverbal activity and group activity. This feature representation enables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent co-occurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from group meetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the co-occurrence features improve the accuracy from 2 % up to 17 %.
{"title":"Personality Trait Classification via Co-Occurrent Multiparty Multimodal Event Discovery","authors":"S. Okada, O. Aran, D. Gática-Pérez","doi":"10.1145/2818346.2820757","DOIUrl":"https://doi.org/10.1145/2818346.2820757","url":null,"abstract":"This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant's nonverbal activity and group activity. This feature representation enables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent co-occurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from group meetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the co-occurrence features improve the accuracy from 2 % up to 17 %.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87982317","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}
Fred Charles, Florian Pecune, Gabor Aranyi, C. Pelachaud, M. Cavazza
User interaction with Embodied Conversational Agents (ECA) should involve a significant affective component to achieve realism in communication. This aspect has been studied through different frameworks describing the relationship between user and ECA, for instance alignment, rapport and empathy. We conducted an experiment to explore how an ECA's non-verbal expression can be controlled to respond to a single affective dimension generated by users as input. Our system is based on the mapping of a high-level affective dimension, approach/avoidance, onto a new ECA control mechanism in which Action Units (AU) are activated through a neural network. Since 'approach' has been associated to prefrontal cortex activation, we use a measure of prefrontal cortex left-asymmetry through fNIRS as a single input signal representing the user's attitude towards the ECA. We carried out the experiment with 10 subjects, who have been instructed to express a positive mental attitude towards the ECA. In return, the ECA facial expression would reflect the perceived attitude under a neurofeedback paradigm. Our results suggest that users are able to successfully interact with the ECA and perceive its response as consistent and realistic, both in terms of ECA responsiveness and in terms of relevance of facial expressions. From a system perspective, the empirical calibration of the network supports a progressive recruitment of various AUs, which provides a principled description of the ECA response and its intensity. Our findings suggest that complex ECA facial expressions can be successfully aligned with one high-level affective dimension. Furthermore, this use of a single dimension as input could support experiments in the fine-tuning of AU activation or their personalization to user preferred modalities.
{"title":"ECA Control using a Single Affective User Dimension","authors":"Fred Charles, Florian Pecune, Gabor Aranyi, C. Pelachaud, M. Cavazza","doi":"10.1145/2818346.2820730","DOIUrl":"https://doi.org/10.1145/2818346.2820730","url":null,"abstract":"User interaction with Embodied Conversational Agents (ECA) should involve a significant affective component to achieve realism in communication. This aspect has been studied through different frameworks describing the relationship between user and ECA, for instance alignment, rapport and empathy. We conducted an experiment to explore how an ECA's non-verbal expression can be controlled to respond to a single affective dimension generated by users as input. Our system is based on the mapping of a high-level affective dimension, approach/avoidance, onto a new ECA control mechanism in which Action Units (AU) are activated through a neural network. Since 'approach' has been associated to prefrontal cortex activation, we use a measure of prefrontal cortex left-asymmetry through fNIRS as a single input signal representing the user's attitude towards the ECA. We carried out the experiment with 10 subjects, who have been instructed to express a positive mental attitude towards the ECA. In return, the ECA facial expression would reflect the perceived attitude under a neurofeedback paradigm. Our results suggest that users are able to successfully interact with the ECA and perceive its response as consistent and realistic, both in terms of ECA responsiveness and in terms of relevance of facial expressions. From a system perspective, the empirical calibration of the network supports a progressive recruitment of various AUs, which provides a principled description of the ECA response and its intensity. Our findings suggest that complex ECA facial expressions can be successfully aligned with one high-level affective dimension. Furthermore, this use of a single dimension as input could support experiments in the fine-tuning of AU activation or their personalization to user preferred modalities.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87401564","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}
We report our image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015. We focus on the sub-challenge of the SFEW 2.0 dataset, where one seeks to automatically classify a set of static images into 7 basic emotions. The proposed method contains a face detection module based on the ensemble of three state-of-the-art face detectors, followed by a classification module with the ensemble of multiple deep convolutional neural networks (CNN). Each CNN model is initialized randomly and pre-trained on a larger dataset provided by the Facial Expression Recognition (FER) Challenge 2013. The pre-trained models are then fine-tuned on the training set of SFEW 2.0. To combine multiple CNN models, we present two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss, and by minimizing the hinge loss. Our proposed method generates state-of-the-art result on the FER dataset. It also achieves 55.96% and 61.29% respectively on the validation and test set of SFEW 2.0, surpassing the challenge baseline of 35.96% and 39.13% with significant gains.
{"title":"Image based Static Facial Expression Recognition with Multiple Deep Network Learning","authors":"Zhiding Yu, Cha Zhang","doi":"10.1145/2818346.2830595","DOIUrl":"https://doi.org/10.1145/2818346.2830595","url":null,"abstract":"We report our image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015. We focus on the sub-challenge of the SFEW 2.0 dataset, where one seeks to automatically classify a set of static images into 7 basic emotions. The proposed method contains a face detection module based on the ensemble of three state-of-the-art face detectors, followed by a classification module with the ensemble of multiple deep convolutional neural networks (CNN). Each CNN model is initialized randomly and pre-trained on a larger dataset provided by the Facial Expression Recognition (FER) Challenge 2013. The pre-trained models are then fine-tuned on the training set of SFEW 2.0. To combine multiple CNN models, we present two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss, and by minimizing the hinge loss. Our proposed method generates state-of-the-art result on the FER dataset. It also achieves 55.96% and 61.29% respectively on the validation and test set of SFEW 2.0, surpassing the challenge baseline of 35.96% and 39.13% with significant gains.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82912237","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}
On a mobile device, the intuitive Focus+Context layout of a detailed view (focus) and perspective/distorted panels on either side (context) is particularly suitable for maximizing the utilization of the limited available display area. Interacting with such a bifocal view requires both fast access to data in the context view and high precision interaction with data in the detailed focus view. We introduce combined modalities that solve this problem by combining the well-known flick-drag gesture-based precise modality with modalities for fast access to data in the context view. The modalities for fast access to data in the context view include direct touch in the context view as well as navigation based on drag gestures, on tilting the device, on side-pressure inputs or by spatially moving the device (dynamic peephole). Results of a comparison experiment of the combined modalities show that the performance can be analyzed according to a 3-phase model of the task: a focus-targeting phase, a transition phase (modality switch) and a cursor-pointing phase. Moreover modalities of the focus-targeting phase based on a discrete mode of navigation control (direct access, pressure sensors as discrete navigation controller) require a long transition phase: this is mainly due to disorientation induced by the loss of control in movements. This effect is significantly more pronounced than the articulatory time for changing the position of the fingers between the two modalities ("homing" time).
{"title":"Multimodal Interaction with a Bifocal View on Mobile Devices","authors":"S. Pelurson, L. Nigay","doi":"10.1145/2818346.2820731","DOIUrl":"https://doi.org/10.1145/2818346.2820731","url":null,"abstract":"On a mobile device, the intuitive Focus+Context layout of a detailed view (focus) and perspective/distorted panels on either side (context) is particularly suitable for maximizing the utilization of the limited available display area. Interacting with such a bifocal view requires both fast access to data in the context view and high precision interaction with data in the detailed focus view. We introduce combined modalities that solve this problem by combining the well-known flick-drag gesture-based precise modality with modalities for fast access to data in the context view. The modalities for fast access to data in the context view include direct touch in the context view as well as navigation based on drag gestures, on tilting the device, on side-pressure inputs or by spatially moving the device (dynamic peephole). Results of a comparison experiment of the combined modalities show that the performance can be analyzed according to a 3-phase model of the task: a focus-targeting phase, a transition phase (modality switch) and a cursor-pointing phase. Moreover modalities of the focus-targeting phase based on a discrete mode of navigation control (direct access, pressure sensors as discrete navigation controller) require a long transition phase: this is mainly due to disorientation induced by the loss of control in movements. This effect is significantly more pronounced than the articulatory time for changing the position of the fingers between the two modalities (\"homing\" time).","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"55 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83334731","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}
Hongwei Ng, Viet Dung Nguyen, Vassilios Vonikakis, Stefan Winkler
This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5% in the validation set and 55.6% in the test set, which compares favorably to the respective 35.96% and 39.13% of the challenge baseline.
{"title":"Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning","authors":"Hongwei Ng, Viet Dung Nguyen, Vassilios Vonikakis, Stefan Winkler","doi":"10.1145/2818346.2830593","DOIUrl":"https://doi.org/10.1145/2818346.2830593","url":null,"abstract":"This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5% in the validation set and 55.6% in the test set, which compares favorably to the respective 35.96% and 39.13% of the challenge baseline.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88925689","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}
T. Wörtwein, Boris Schauerte, Karin Müller, R. Stiefelhagen
In this demonstration, we show a web-based sonification platform that allows blind users to interactively experience various information using two nowadays widespread technologies: modern web browsers that implement high-level JavaScript APIs and touch-sensitive displays. This way, blind users can easily access information such as, for example, maps or graphs. Our current prototype provides various sonifications that can be switched depending on the image type and user preference. The prototype runs in Chrome and Firefox on PCs, smart phones, and tablets.
{"title":"Interactive Web-based Image Sonification for the Blind","authors":"T. Wörtwein, Boris Schauerte, Karin Müller, R. Stiefelhagen","doi":"10.1145/2818346.2823298","DOIUrl":"https://doi.org/10.1145/2818346.2823298","url":null,"abstract":"In this demonstration, we show a web-based sonification platform that allows blind users to interactively experience various information using two nowadays widespread technologies: modern web browsers that implement high-level JavaScript APIs and touch-sensitive displays. This way, blind users can easily access information such as, for example, maps or graphs. Our current prototype provides various sonifications that can be switched depending on the image type and user preference. The prototype runs in Chrome and Firefox on PCs, smart phones, and tablets.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82066242","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}
Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.
{"title":"Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit","authors":"Asif Iqbal, C. Busso, N. Gans","doi":"10.1145/2818346.2820741","DOIUrl":"https://doi.org/10.1145/2818346.2820741","url":null,"abstract":"Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91189600","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}
M. Worsley, Stefan Scherer, Louis-Philippe Morency, Paulo Blikstein
Multimodal analysis has long been an integral part of studying learning. Historically multimodal analyses of learning have been extremely laborious and time intensive. However, researchers have recently been exploring ways to use multimodal computational analysis in the service of studying how people learn in complex learning environments. In an effort to advance this research agenda, we present a comparative analysis of four different data segmentation techniques. In particular, we propose affect- and pose-based data segmentation, as alternatives to human-based segmentation, and fixed-window segmentation. In a study of ten dyads working on an open-ended engineering design task, we find that affect- and pose-based segmentation are more effective, than traditional approaches, for drawing correlations between learning-relevant constructs, and multimodal behaviors. We also find that pose-based segmentation outperforms the two more traditional segmentation strategies for predicting student success on the hands-on task. In this paper we discuss the algorithms used, our results, and the implications that this work may have in non-education-related contexts.
{"title":"Exploring Behavior Representation for Learning Analytics","authors":"M. Worsley, Stefan Scherer, Louis-Philippe Morency, Paulo Blikstein","doi":"10.1145/2818346.2820737","DOIUrl":"https://doi.org/10.1145/2818346.2820737","url":null,"abstract":"Multimodal analysis has long been an integral part of studying learning. Historically multimodal analyses of learning have been extremely laborious and time intensive. However, researchers have recently been exploring ways to use multimodal computational analysis in the service of studying how people learn in complex learning environments. In an effort to advance this research agenda, we present a comparative analysis of four different data segmentation techniques. In particular, we propose affect- and pose-based data segmentation, as alternatives to human-based segmentation, and fixed-window segmentation. In a study of ten dyads working on an open-ended engineering design task, we find that affect- and pose-based segmentation are more effective, than traditional approaches, for drawing correlations between learning-relevant constructs, and multimodal behaviors. We also find that pose-based segmentation outperforms the two more traditional segmentation strategies for predicting student success on the hands-on task. In this paper we discuss the algorithms used, our results, and the implications that this work may have in non-education-related contexts.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88539425","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}
J. Schneider, D. Börner, P. V. Rosmalen, M. Specht
The Presentation Trainer is a multimodal tool designed to support the practice of public speaking skills, by giving the user real-time feedback about different aspects of her nonverbal communication. It tracks the user's voice and body to interpret her current performance. Based on this performance the Presentation Trainer selects the type of intervention that will be presented as feedback to the user. This feedback mechanism has been designed taking in consideration the results from previous studies that show how difficult it is for learners to perceive and correctly interpret real-time feedback while practicing their speeches. In this paper we present the user experience evaluation of participants who used the Presentation Trainer to practice for an elevator pitch, showing that the feedback provided by the Presentation Trainer has a significant influence on learning.
{"title":"Presentation Trainer, your Public Speaking Multimodal Coach","authors":"J. Schneider, D. Börner, P. V. Rosmalen, M. Specht","doi":"10.1145/2818346.2830603","DOIUrl":"https://doi.org/10.1145/2818346.2830603","url":null,"abstract":"The Presentation Trainer is a multimodal tool designed to support the practice of public speaking skills, by giving the user real-time feedback about different aspects of her nonverbal communication. It tracks the user's voice and body to interpret her current performance. Based on this performance the Presentation Trainer selects the type of intervention that will be presented as feedback to the user. This feedback mechanism has been designed taking in consideration the results from previous studies that show how difficult it is for learners to perceive and correctly interpret real-time feedback while practicing their speeches. In this paper we present the user experience evaluation of participants who used the Presentation Trainer to practice for an elevator pitch, showing that the feedback provided by the Presentation Trainer has a significant influence on learning.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82342851","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}
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