Researcher:
Bayramoğlu, Öykü Zeynep

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Master Student

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Öykü Zeynep

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Bayramoğlu

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Bayramoğlu, Öykü Zeynep

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2021 - 20232

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Researcher Of Publications: search.filters.isAuthorOfPublication.16236a4a-efa9-416d-928b-acf3f2a157af

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    The eHRI database: a multimodal database of engagement in human-robot interactions
    (Springer, 2023) N/A; N/A; N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Kesim, Ege; Numanoğlu, Tuğçe; Bayramoğlu, Öykü Zeynep; Türker, Bekir Berker; Hussain, Nusrah; Sezgin, Tevfik Metin; Yemez, Yücel; Erzin, Engin; Master Student; Master Student; Master Student; Researcher; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; 18632; 107907; 34503
    We present the engagement in human-robot interaction (eHRI) database containing natural interactions between two human participants and a robot under a story-shaping game scenario. The audio-visual recordings provided with the database are fully annotated at a 5-intensity scale for head nods and smiles, as well as with speech transcription and continuous engagement values. In addition, we present baseline results for the smile and head nod detection along with a real-time multimodal engagement monitoring system. We believe that the eHRI database will serve as a novel asset for research in affective human-robot interaction by providing raw data, annotations, and baseline results.
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    PublicationOpen Access
    Engagement rewarded actor-critic with conservative Q-learning for speech-driven laughter backchannel generation
    (Association for Computing Machinery (ACM), 2021) Department of Computer Engineering; Bayramoğlu, Öykü Zeynep; Erzin, Engin; Sezgin, Tevfik Metin; Yemez, Yücel; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and Engineering; N/A; 34503; 18632; 107907
    We propose a speech-driven laughter backchannel generation model to reward engagement during human-agent interaction. We formulate the problem as a Markov decision process where speech signal represents the state and the objective is to maximize human engagement. Since online training is often impractical in the case of human-agent interaction, we utilize the existing human-to-human dyadic interaction datasets to train our agent for the backchannel generation task. We address the problem using an actor-critic method based on conservative Q-learning (CQL), that mitigates the distributional shift problem by suppressing Q-value over-estimation during training. The proposed CQL based approach is evaluated objectively on the IEMOCAP dataset for laughter generation task. When compared to the existing off-policy Q-learning methods, we observe an improved compliance with the dataset in terms of laugh generation rate. Furthermore, we show the effectiveness of the learned policy by estimating the expected engagement using off-policy policy evaluation techniques.