In an interaction, cues for detecting threatening situations could be changes in the intonation or loudness indicating dangerous situations. Robots should be able to perceive and react to such signals and change their behaviour to avoid the threatening situation or, in case the threat cannot yet be identified, be alert and act more carefully. In order to achieve this goal, this project aims at identifying prosodic features using robust neural sound localisation in order to change the robots behaviour or trigger the inclusion of other sensory pathways.
The outcome of this project will be a new learning approach integrating perception of prosodic features to identify and avoid possibly threatening situations by subsequently changing the robot’s behaviour or trigger the inclusion of other sensory pathways to identify the threat.
Lakomkin, E., Bothe, C., and Wermter, S. (2017, September).
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection.
To be appear in Workshop (WASSA) at EMNLP-2017.
Lakomkin, E., Weber, C., Wermter, S. (2017, April).
Automatically augmenting an emotion dataset improves classification using audio.
15th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Valencia, Spain.
Short Curriculum Vitae
- Since February 2016: Research Associate (PhD Student: SECURE Project) at Knowledge Technology Research Group, Department of Computer Science, University of Hamburg, Germany
- M.Sc. in Computer Science, Moscow State Technical University n.a. Bauman , Moscow, Russia. Faculty – Informatics and control systems, department - Automatic Information Processing and Control Systems, diploma in computer engineering, class of 2011, GPA 4,4 (of 5)
- Nanyang Technological University, researcher and developer, Summer Research Internship, School of Computer Engineering
- Nanyang Technological University, Research associate, Computer Linguistics and Bioinformatics
- 2nd place in Spoken Language Recognition contest at TopCoder (https://community.topcoder.com/longcontest/stats/?module=ViewOverview&rd=16498)
- 3rd place in Harvard Banner Disease Recognition Competition contest at TopCoder
- Garage48 hackaton winner
- Apps4Russia contest winner, nomination “Comfortable city”
Research Associate SECURE Project
Knowledge Technology Group (WTM)
Department of Informatics
University of Hamburg
22527 Hamburg, Germany
Phone: +49 40 42883 2318
Fax: +49 40 42883 2515
lakomkin at informatik.uni-hamburg.de