M.Sc. Dmitrii Fedotov
Research
PhD Thesis
Emotion recognition has been of interest for researchers for a long period of time and during the last two decades it has received a noticeable development due to hardware and software improvements, as well as the increasing demand on intelligent conversational agents. In spite of the fact that modern emotion recognition research aims towards spontaneous data, recorded not in laboratory conditions, and real world applications, it is often being performed in an isolated manner. This implies that the recognition is done without accounting for previous actions or emotional status of a user, information about his/her interlocutor (if any) and environment while designing the pipeline. All these aspects and sources of information play an important role in defining or affecting the current mood and emotional status of the user; hence they should be analyzed in order to get a precise and comprehensive estimation. My thesis outlines novel approaches to integrate contextual information for improving the performance of automatic emotion recognition systems.
G. Sogancioglu, O. Verkholyak, H. Kaya, D. Fedotov, T. Cadee, A. Ali Salah and A. Karpov
Is everything fine, grandma? Acoustic and linguistic modeling for robust elderly speech emotion recognition
Proceedings of INTERSPEECH, pp. 2097-2101, 2020
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O. Akhtiamov, D. Fedotov and W. Minker
A Comparative Study of Classical and Deep Classifiers for Textual Addressee Detection in Human-Human-Machine Conversations
International Conference on Speech and Computer (SPECOM), Springer, pp. 20-30, 2019
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D. Fedotov, O. Verkholyak and A. Karpov
Context time-continuous speech emotion recognition in Russian based on recurrent neural networks
Analysis of Spoken Russian Lanuguage [Analiz razgovornoy russkoy rechi], pp. 96-99, 2019
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D. Fedotov, Y. Matsuda and W. Minker
From Smart to Personal Environment: Integrating Emotion Recognition into Smart Houses
2019 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 943-948, 2019
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O. Verkholyak, D. Fedotov, H. Kaya, Y. Zhang and A. Karpov
Hierarchical Two-level Modelling of Emotional States in Spoken Dialog Systems
2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6700-6704, 2019
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H. Kaya, D. Fedotov, D. Dresvyanskiy, M. Doyran, D. Mamontov, M. Markitantov, A. Salah, E. Kavcar, A. Karpov and A. Ali Salah
Predicting depression and emotions in the cross-roads of cultures, para-linguistics, and non-linguistics
2019
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D. Fedotov, Y. Matsuda, Y. Takahashi, Y. Arakawa and W. Minker
Towards Real-Time Contextual Touristic Emotion and Satisfaction Estimation with Wearable Devices
2019 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 358-360, 2019
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D. Ivanko, A. Karpov, D. Fedotov, I. Kipyatkova, D. Ryumin, D. Ivanko, W. Minker and M. Zelezny
Multimodal speech recognition: increasing accuracy using high speed video data
Journal on Multimodal User Interfaces, Vol. 12, Num. 4, pp. 319--328, December 2018
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Y. Matsuda, D. Fedotov, Y. Takahashi, Y. Arakawa, K. Yasumoto and W. Minker
EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data
Sensors, Vol. 18, Num. 11, November 2018
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D. Ivanko, D. Fedotov and A. Karpov
Accuracy increase for automatic visual Russian speech recognition: viseme classes optimization
Scientific and Technical Journal of Information Technologies, Mechanics and Optics, Vol. 18, Num. 2, pp. 346-349, 2018
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Y. Takahashi, Y. Matsuda, D. Fedotov, Y. Arakawa, W. Minker and K. Yasumoto
Analysis of Tourist's Unconscious Gesture Toward Inner State Estimation During Sightseeing
IEICE, Technical Committee on Human Probes (HPB), 2018
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D. Fedotov, H. Kaya and A. Karpov
Context Modeling for Cross-corpus Dimensional Acoustic Emotion Recognition: Challenges and Mixup
Proceedings of the 20th International Conference on Speech and Computer, SPECOM 2018, 2018
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D. Fedotov, D. Ivanko, M. Sidorov and W. Minker
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition
Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018), 2018
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Y. Matsuda, D. Fedotov, Y. Takahashi, Y. Arakawa, K. Yasumoto and W. Minker
EmoTour: Multimodal Emotion Recognition using Physiological and Audio-Visual Features
Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 946-951, 2018
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Y. Matsuda, D. Fedotov, Y. Takahashi, Y. Arakawa, K. Yasumoto and W. Minker
Estimating User Satisfaction Impact in Cities using Physical Reaction Sensing and Multimodal Dialogue System
Proceedings of the 9th International Workshop On Spoken Dialogue Systems (IWSDS), 2018
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H. Kaya, D. Fedotov, A. Yesilkanat, O. Verkholyak, Y. Zhang and A. Karpov
LSTM based Cross-corpus and Cross-task Acoustic Emotion Recognition
Proc. Interspeech 2018, pp. 521-525, 2018
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D. Fedotov, O. Perepelkina, E. Kazimirova, M. Konstantinova and W. Minker
Multimodal approach to engagement and disengagement detection with highly imbalanced in-the-wild data
Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data, ACM, Boulder, Colorado, Series: MCPMD '18, 2018
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D. Fedotov, Y. Matsuda, Y. Takahashi, Y. Arakawa, K. Yasumoto and W. Minker
Towards Estimating Emotions and Satisfaction Level of Tourist based on Eye Gaze and Head Movement
Smart Computing (SMARTCOMP), 2018 IEEE International Conference on, 2018
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D. Fedotov, M. Sidorov and W. Minker
Context-Awared Models in Time-Continuous Multidimensional Affect Recognition
International Conference on Interactive Collaborative Robotics, 2017
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Sommersemester 2018 - Benutzerschnittstellen
Open Topics
For open topics for Diploma, Master or Bachelor Theses and Studienarbeiten, please come and see me in my office or write an e-mail.
In Progress / Completed
Master Thesis
Autoregressive Exogenous Models for Time-Continuous Emotion Recognition (Euler Program)
2019
Master Thesis
Deep Learning Based Feature Representation for Multimodal Emotion Recognition (Euler Program)
2019
Master Thesis
On Complementing Emotion Recognition in Dyadic Interactions (Euler Program)
2019
Master Thesis
Visual emotion recognition in the wild at multi-user and group levels
2019
Master Thesis
Analysis of emotional context in social networks for guidance systems improvement (Euler Program)
2018
Master Thesis
End-to-end time-continuous emotion recognition for spontaneous interactions
2018
Master Thesis
Bayesian optimisation of LSTM networks for emotion recognition (Euler Program)
2017
Master Thesis
Recurrent neural network models for time-continuous multi-label emotion recognition (Euler Program)
2017