UDK 519.87
EFFECTIVE SPEECH-BASED STUDENT AUTHENTICATION PROCEDURE IN DISTANCE LEARNING
C. Y. Brester [1], S. R. Vishnevskaya [2], O. E. Semenkina [3], M. Y. Sidorov [4]
[1], [2], [3] Siberian State Aerospace University named after academician M. F. Reshetnev 31, Krasnoyarsky Rabochy Аv., Krasnoyarsk, 660014, Russian Federation Е-mail: 1christina.bre@yandex.ru, 2vishni@ngs.ru, 3semenkina.olga@mail.ru [4] Ulm University 43, Albert-Einstein-Allee, Ulm, 89081, Germany Е-mail: maxim.sidorov@uni-ulm.de
Nowadays it is almost impossible to find a university that does not provide its students with online courses or correspondence education. Due to various advantages, distance learning has attracted more and more people in recent years. As a result, some of the requirements that this educational format has to satisfy have been included in legislation systems. The necessity to authenticate students remotely is presented as a compulsory procedure in many official documents. When teachers are deprived of face-to-face contact with their students, there is a need to find an appropriate way to verify their personality distantly. In this paper we propose the speech-based student authentication procedure which operates with some acoustic characteristics extracted from voice signals. However, there is one crucial question related to the classification model providing high performance. It is almost impossible for the online systems to vary classifiers and determine the most effective one while interacting with a user. Therefore, to increase the reliability of our proposal we elaborated some classification schemes based on collective decision making to take into account predictions of different classifiers. To prove the effectiveness of this approach, we used a number of multi-lingual corpora (German, English, Japanese). According to the results obtained, a high level of speaker recognition was achieved (up to 100 % of F-score values). The developed algorithmic schemes provide a guaranteed level of effectiveness and might be used as a reliable alternative to the occasional choice of a classification model.
distance learning, speech-based student authentication, classifier, collective decision making.
References
- Higher Education Opportunity Act (Public Law 110-315), Aug. 14, 2008, USA.
- Eyben F., Wöllmer M., and Schuller B. Opensmile: the munich versatile and fast opensource audio feature extractor. Proceedings of the international conference on Multimedia, 2010. ACM, P. 1459–1462.
- Boersma P. Praat, a system for doing phonetics by computer. Glot international, 2002, no. 5(9/10), p. 341–345.
- Brester C., Semenkin E., Sidorov M., Minker W. Self-adaptive multi-objective genetic algorithms for feature selection. Proceedings of International Conference on Engineering and Applied Sciences Optimization (OPT-i’14), 2014, p. 1838–1846.
- Sidorov M., Brester Ch., Minker W., Semenkin E. Speech-Based Emotion Recognition: Feature Selection by Self-Adaptive Multi-Criteria Genetic Algorithm. LREC, 2014, p. 3481–3485.
- Khritonenko D. I., Semenkin E. S. Distributed self-configuring evolutionary algorithms for artificial neural networks design. Vestnik SibGAU. 2013, no. 4 (50), p. 112–116.
- Stanovov V. V., Semenkin E. S. [Self-adjusted evolutionary algorithm for fuzzy rules design in classification problems]. Sistemy upravleniya i informatsionnye tekhnologii. 2014, no. 3 (57), p. 30–35 (In Russ).
- Akhmedova Sh., Semenkin E. Co-Operation of Biology Related Algorithms Meta-Heuristic in ANN-Based Classifiers Design. Proceedings of the World Congress on Computational Intelligence (WCCI’14), 2014.
- Popov E. A., Semenkina M. E., Lipinskiy L. V. [Decision making with intelligent information technology ensemble]. Vestnik SibGAU. 2012, no. 5 (45), p. 95–99 (In Russ).
- Goutte C., Gaussier E. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research, 2005, p. 345–359.
- Burkhardt F., Paeschke A., Rolfes M., Sendlmeier W. F., and Weiss B. A database of german emotional speech. In Interspeech, 2005, p. 1517–1520.
- Haq S., Jackson P. Machine Audition: Principles, Algorithms and Systems, chapter Multimodal Emotion Recognition. IGI Global, Hershey PA. Aug. 2010, p. 398–423.
- Grimm M., Kroschel K., and Narayanan S. The vera am mittag german audio-visual emotional speech database. In Multimedia and Expo, IEEE International Conference on, IEEE, 2008, p. 865–868.
- Mori H., Satake T., Nakamura M., and Kasuya H. Constructing a spoken dialogue corpus for studying paralinguistic information in expressive conversation and analyzing its statistical/acoustic characteristics. Speech Communication, 53, 2011.
- Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. H. The WEKA Data Mining Software: An Update, SIGKDD Explorations, 2009, Vol. 11, Iss. 1.
Brester Christina Yurievna – junior research fellow, Siberian State Aerospace University named after academician M. F. Reshetnev. Е-mail: christina.bre@yandex.ru
Vishnevskaya Sophia Romanovna – Cand. Sc., Docent, Head of the Higher Mathematics Department, Siberian State Aerospace University named after academician M. F. Reshetnev. Е-mail: vishni@ngs.ru
Semenkina Olga Ernestovna – Dr. Sc., Professor, Professor of the Higher Mathematics Department, Siberian State Aerospace University named after academician M. F. Reshetnev. Е-mail: semenkina.olga@mail.ru
Sidorov Maxim Yurievich – research fellow, Ulm University. Е-mail: maxim.sidorov@uni-ulm.de
