Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Savchenko, Andrey V.; Belova, Natalya S.

doi:10.1515/amcs-2015-0065

Abstract

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.

References

Asadpour, V., Homayounpour, M.M. and Towhidkhah, F. (2011). Audio-visual speaker identification using dynamic facial movements and utterance phonetic content, Applied Soft Computing11(2): 2083–2093.10.1016/j.asoc.2010.07.007
Search in Google Scholar
Benesty, J., Sondhi, M.M. and Huang, Y. (2008). Springer Handbook of Speech Processing, Springer, Berlin.10.1007/978-3-540-49127-9
Search in Google Scholar
Borovkov, A.A. (1998). Mathematical Statistics, Gordon and Breach Science Publishers, Amsterdam.
Search in Google Scholar
Bottou, L., Fogelman Soulie, F., Blanchet, P. and Lienard, J. (1990). Speaker-independent isolated digit recognition: Multilayer perceptrons vs. dynamic time warping, Neural Networks3(4): 453–465.
Search in Google Scholar
Ciresan, D., Meier, U., Masci, J. and Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification, Neural Networks32: 333–338.10.1016/j.neunet.2012.02.02322386783
Search in Google Scholar
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.
Search in Google Scholar
Gray, R., Buzo, A., Gray, A., Jr. and Matsuyama, Y. (1980). Distortion measures for speech processing, IEEE Transactions on Acoustics, Speech and Signal Processing28(4): 367–376.10.1109/TASSP.1980.1163421
Search in Google Scholar
Haykin, S.O. (2008). Neural Networks and Learning Machines, 3rd Edn., Prentice Hall, Harlow.
Search in Google Scholar
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 Magazine29(6): 82–97.10.1109/MSP.2012.2205597
Search in Google Scholar
Hinton, G.E., Osindero, S. and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets, Neural Computation18(7): 1527–1554.10.1162/neco.2006.18.7.152716764513
Search in Google Scholar
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.
Search in Google Scholar
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.
Search in Google Scholar
Kullback, S. (1997). Information Theory and Statistics, Dover Publications, New York, NY.
Search in Google Scholar
LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning, Nature521(7553): 436–444.10.1038/nature1453926017442
Search in Google Scholar
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE86(11): 2278–2324.10.1109/5.726791
Search in Google Scholar
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. 828–837.10.1007/978-3-540-74549-5_87
Search in Google Scholar
Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision60(2): 91–110.10.1023/B:VISI.0000029664.99615.94
Search in Google Scholar
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.
Search in Google Scholar
Merialdo, B. (1988). Multilevel decoding for very-large-size-dictionary speech recognition, IBM Journal of Research and Development32(2): 227–237.10.1147/rd.322.0227
Search in Google Scholar
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.
Search in Google Scholar
Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer-Verlag, Berlin/Heidelberg.
Search in Google Scholar
Sas, J. and Żołnierek, A. (2013). Pipelined language model construction for Polish speech recognition, International Journal of Applied Mathematics and Computer Science23(3): 649–668, DOI: 10.2478/amcs-2013-0049.10.2478/amcs-2013-0049
Search in Google Scholar
Savchenko, A.V. (2012). Directed enumeration method in image recognition, Pattern Recognition45(8): 2952–2961.10.1016/j.patcog.2012.02.011
Search in Google Scholar
Savchenko, A.V. (2013a). Phonetic words decoding software in the problem of Russian speech recognition, Automation and Remote Control74(7): 1225–1232.10.1134/S000511791307014X
Search in Google Scholar
Savchenko, A.V. (2013b). Probabilistic neural network with homogeneity testing in recognition of discrete patterns set, Neural Networks46: 227–241.10.1016/j.neunet.2013.06.003
Search in Google Scholar
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): 34–42.10.3103/S1060992X14010068
Search in Google Scholar
Specht, D.F. (1990). Probabilistic neural networks, Neural Networks3(1): 109–118.10.1016/0893-6080(90)90049-Q
Search in Google Scholar
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 Science20(1): 135–147, DOI: 10.2478/v10006-010-0010-x.10.2478/v10006-010-0010-x
Search in Google Scholar
Tan, X., Chen, S., Zhou, Z.-H. and Zhang, F. (2006). Face recognition from a single image per person: A survey, Pattern Recognition39(9): 1725–1745.10.1016/j.patcog.2006.03.013
Search in Google Scholar
Theodoridis, S. and Koutroumbas, K. (2008). Pattern Recognition, 4th Edn., Academic Press, Burlington, MA/London.
Search in Google Scholar
Zhou, E., Cao, Z. and Yin, Q. (2015). Naive-deep face recognition: Touching the limit of LFW benchmark or not?, CoRRabs/1501.04690.
Search in Google Scholar

Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Abstract

Paradigm

My account