Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2015 | 25 | 4 | 915-925
Tytuł artykułu

Statistical testing of segment homogeneity in classification of piecewise-regular objects

Treść / Zawartość
Warianty tytułu
Języki publikacji
The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and a statistical approach, we reduce the task to a problem of composite hypothesis testing of segment homogeneity. Several nearest-neighbor criteria are implemented, and for some of them the well-known special cases (e.g., the Kullback-Leibler minimum information discrimination principle, the probabilistic neural network) are highlighted. It is experimentally shown that the proposed approach improves the accuracy when compared with contemporary classifiers.
Opis fizyczny
  • Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova St., Nizhny Novgorod 603093, Russia
  • Faculty of Computer Science, National Research University Higher School of Economics, 20 Myasnitskaya St., Moscow 101000, Russia
  • Asadpour, V., Homayounpour, M.M. and Towhidkhah, F. (2011). Audio-visual speaker identification using dynamic facial movements and utterance phonetic content, Applied Soft Computing 11(2): 2083-2093.
  • Benesty, J., Sondhi, M.M. and Huang, Y. (2008). Springer Handbook of Speech Processing, Springer, Berlin.
  • Borovkov, A.A. (1998). Mathematical Statistics, Gordon and Breach Science Publishers, Amsterdam.
  • Bottou, L., Fogelman Soulie, F., Blanchet, P. and Lienard, J. (1990). Speaker-independent isolated digit recognition: Multilayer perceptrons vs. dynamic time warping, Neural Networks 3(4): 453-465.
  • Ciresan, D., Meier, U., Masci, J. and Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification, Neural Networks 32: 333-338.
  • Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, CA, USA, pp. 886-893.
  • Gray, R., Buzo, A., Gray, A., Jr. and Matsuyama, Y. (1980). Distortion measures for speech processing, IEEE Transactions on Acoustics, Speech and Signal Processing 28(4): 367-376.
  • Haykin, S.O. (2008). Neural Networks and Learning Machines, 3rd Edn., Prentice Hall, Harlow.
  • Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. and Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, IEEE Signal Processing Magazine 29(6): 82-97.
  • Hinton, G.E., Osindero, S. and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets, Neural Computation 18(7): 1527-1554.
  • Huang, J.-T., Li, J., Yu, D., Deng, L. and Gong, Y. (2013). Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, pp. 7304-7308.
  • Janakiraman, R., Kumar, J. and Murthy, H. (2010). Robust syllable segmentation and its application to syllable-centric continuous speech recognition, Proceedings of the National Conference on Communications, NCC 2010, Chennai, India, pp. 1-5.
  • Kullback, S. (1997). Information Theory and Statistics, Dover Publications, New York, NY.
  • LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning, Nature 521(7553): 436-444.
  • LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11): 2278-2324.
  • Liao, S., Zhu, X., Lei, Z., Zhang, L. and Li, S.Z. (2007). Learning multi-scale block local binary patterns for face recognition, in S.-W. Lee and S.Z. Li (Eds.), Advances in Biometrics, Lecture Notes in Computer Science, Vol. 4642, Springer, Berlin/Heidelberg, pp. 828-837.
  • Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60(2): 91-110.
  • Martins, A.F.T., Figueiredo, M.A.T., Aguiar, P.M.Q., Smith, N.A. and Xing, E.P. (2008). Nonextensive entropic kernels, Proceedings of the 25th International Conference on Machine Learning, ICML '2008, New York, NY, USA, pp. 640-647.
  • Merialdo, B. (1988). Multilevel decoding for very-large-size-dictionary speech recognition, IBM Journal of Research and Development 32(2): 227-237.
  • Pfau, T. and Ruske, G. (1998). Estimating the speaking rate by vowel detection, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998, Seattle, WA, USA, Vol. 2, pp. 945-948.
  • Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer-Verlag, Berlin/Heidelberg.
  • Sas, J. and Żołnierek, A. (2013). Pipelined language model construction for Polish speech recognition, International Journal of Applied Mathematics and Computer Science 23(3): 649-668, DOI: 10.2478/amcs-2013-0049.
  • Savchenko, A.V. (2012). Directed enumeration method in image recognition, Pattern Recognition 45(8): 2952-2961.
  • Savchenko, A.V. (2013a). Phonetic words decoding software in the problem of Russian speech recognition, Automation and Remote Control 74(7): 1225-1232.
  • Savchenko, A.V. (2013b). Probabilistic neural network with homogeneity testing in recognition of discrete patterns set, Neural Networks 46: 227-241.
  • Savchenko, A.V. and Khokhlova, Y.I. (2014). About neural-network algorithms application in viseme classification problem with face video in audiovisual speech recognition systems, Optical Memory and Neural Networks (Information Optics) 23(1): 34-42.
  • Specht, D.F. (1990). Probabilistic neural networks, Neural Networks 3(1): 109-118.
  • Świercz, E. (2010). Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution, International Journal of Applied Mathematics and Computer Science 20(1): 135-147, DOI: 10.2478/v10006-010-0010-x.
  • Tan, X., Chen, S., Zhou, Z.-H. and Zhang, F. (2006). Face recognition from a single image per person: A survey, Pattern Recognition 39(9): 1725-1745.
  • Theodoridis, S. and Koutroumbas, K. (2008). Pattern Recognition, 4th Edn., Academic Press, Burlington, MA/London.
  • Zhou, E., Cao, Z. and Yin, Q. (2015). Naive-deep face recognition: Touching the limit of LFW benchmark or not?, CoRR abs/1501.04690.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.