Researcher:
Buçinca, Zana

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Master Student

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Zana

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Buçinca

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Buçinca, Zana

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20173

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Researcher Of Publications: search.filters.isAuthorOfPublication.c9097002-8c16-4f9e-8cea-6286d8249e66

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    Analysis of engagement and user experience with a laughter responsive social robot
    (Isca-int Speech Communication assoc, 2017) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Türker, Bekir Berker; Buçinca, Zana; Erzin, Engin; Yemez, Yücel; Sezgin, Tevfik Metin; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 34503; 107907; 18632
    We explore the effect of laughter perception and response in terms of engagement in human-robot interaction. We designed two distinct experiments in which the robot has two modes: laughter responsive and laughter non-responsive. in responsive mode, the robot detects laughter using a multimodal real-time laughter detection module and invokes laughter as a backchannel to users accordingly. in non-responsive mode, robot has no utilization of detection, thus provides no feedback. in the experimental design, we use a straightforward question-answer based interaction scenario using a back-projected robot head. We evaluate the interactions with objective and subjective measurements of engagement and user experience.
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    Real-time audiovisual laughter detection
    (Ieee, 2017) N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Türker, Bekir Berker; Buçinca, Zana; Sezgin, Tevfik Metin; Yemez, Yücel; Erzin, Engin; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 18632; 107907; 34503
    Laughter detection is an essential aspect towards effective human-computer interaction. This work primarily addresses the problem of laughter detection in a real-time environment. We utilize annotated audio and visual data collected from a Kinect sensor to identify discriminative features for audio and video, separately. We show how the features can be used with classifiers such as support vector machines (SVM). The two modalities are then fused into a single output to form a decision. We test our setup by emulating real-time data with Kinect sensor, and compare the results with the offline version of the setup. Our results indicate that our laughter detection system gives a promising performance for a real-time human-computer interactions.
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    Real-time audiovisual laughter detection
    (Institute of Electrical and Electronics Engineers (IEEE), 2017) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Türker, Bekir Berker; Buçinca, Zana; Sezgin, Tevfik Metin; Yemez, Yücel; Erzin, Engin; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 18632; 107907; 34503
    Laughter detection is an essential aspect towards effective human-computer interaction. This work primarily addresses the problem of laughter detection in a real-time environment. We utilize annotated audio and visual data collected from a Kinect sensor to identify discriminative features for audio and video, separately. We show how the features can be used with classifiers such as support vector machines (SVM). The two modalities are then fused into a single output to form a decision. We test our setup by emulating real-time data with Kinect sensor, and compare the results with the offline version of the setup. Our results indicate that our laughter detection system gives a promising performance for a real-time human-computer interactions.