Abstract
In this work, we address some issues in the classification of varying length patterns of speech represented as sets of continuous-valued feature vectors using kernel methods. Kernels designed for varying length patterns are called as dynamic kernels. We propose two dynamic kernels namely segment-level pyramid match kernel (SLPMK) and segment-level probabilistic sequence kernel (SLPSK) for classification of long duration speech, represented as varying length sets of feature vectors using extreme learning machine (ELM). SLPMK and SLPSK are designed by partitioning the speech signal into increasingly finer segments and matching the corresponding segments. SLPSK is built upon a set of Gaussian basis functions, where half of the basis functions contain class-specific information while the other half implicates the common characteristics of all the speech utterances of all classes. The computational complexity of SVM training algorithms is usually intensive, which is at least quadratic with respect to the number of training examples. It is difficult to deal with the immense amount of data using traditional SVMs. For reducing the training time of classifier we propose to use a simple algorithm namely ELM. ELM refers to a wider type of generalized single hidden layer feedforward networks (SLFNs) whose hidden layer need not be tuned. In our work, we proposed to explore kernel based ELM to exploit dynamic kernels. We study the performance of the ELM-based classifiers using the proposed SLPSK and SLPMK for speech emotion recognition and speaker identification tasks and compare with other kernels for varying length patterns. Experimental studies showed that proposed ELM-based approach offer a 10–12% of relative improvement over baseline approach, and a 3–9% relative improvement over ELMs/SVMs using other state-of-the-art dynamic kernels.
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Alexandos, I., Tefas, A., & Pitas, ioannis. (2015). On the kernel extreme learning machine classifiers. Pattern Recognition Letters, 54, 11–17.
Allwein, E. L., Schapire, R. E., & Singer, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1(Dec), 113–141.
Boughorbel, S., Tarel, J. P., & Boujemaa, N. (2005). The intermediate matching kernel for image local features. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2005) (pp. 889–894), Montreal
Burkhardt, F., Paeschke, A., Rolfes, M., & Weiss, W. S. B. (2005). A database of German emotional speech. In Proceedings of INTERSPEECH (pp. 1517–1520), Lisbon.
Campbell, W. M., & Sturim, D. D. E. (2006). Support vector machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters, 13(5), 308–311.
Chang, C. C., & Linm, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27. http://www.csie.ntu.edu.tw/cjlin/libsvm.
Chen, Yh., Lopez-Moreno, I., Sainath, T., Visontai, M., Alvarez, R., & Parada, C. (2015). Locally connected and convolutional neural networks for small footprint speaker recognition. In Proceedings of INTERSPEECH (pp. 1136–1140), Dresden.
Chorowski, J., Wang, J., & Zurada, J. M. (2014). Review and performance comparison of svm-and elm-based classifiers. Neurocomputing, 128, 507–516.
Dileep, A. D., & Chandra Sekhar, C. (2012). Speaker recognition using pyramid match kernel based support vector machines. Internatiional Journal for Speech Technology, 15(3), 365–379.
Dileep, A. D., & Chandra Sekhar, C. (2014). GMM-based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines. IEEE Transactions on Neural Networks and Learning Systems, 25(8), 1421–1432.
Gemert, Veenman C. J., Smeulders, A. W. M., & Geusebroek, J. M. (2010). Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(17), 1271–1283.
Gordon, G., & Tibshirani, R. (2012). Karush-kuhn-tucker conditions. Optimization, 10(725/36), 725.
Grauman, K., & Darrell, T. (2007). The pyramid match kernel: Efficient learning with sets of features. The Journal of Machine Learning Research, 8, 725–760.
Gupta, S., Dileep, A. D., & Thenkanidiyoor, V. (2016a). Segment-level pyramid match kernels for the classification of varying length patterns of speech using svms. In Signal Processing Conference (EUSIPCO), 2016 24th European, IEEE (pp. 2030–2034).
Gupta, S., Thenkanidiyoor, V., & Dileep, A. D. (2016b). Segment-level probabilistic sequence kernel based support vector machines for classification of varying length patterns of speech. In International Conference on Neural Information Processing (pp. 321–328). New York: Springer.
Huang, G. (2014). An insight into extreme learning machines: Random neurons, random features and kernels. Cognitive Computation, 6(3), 376–390. https://doi.org/10.1007/s12559-014-9255-2.
Huang, G. B., Chen, L., & Siew, C. K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17(4), 879–892.
Huang, G. B., Zhou, H., Ding, X., et al. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, B (Cybernetics), 42(2), 513–529.
Lee, K. A., HTK You, C. H. (2007). A GMM-based probabilistic sequence kernel for speaker verification. In Proceedings of INTERSPEECH, (pp. 294–297), Antwerp.
Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), (vol. 2, pp. 2169–2178), New York.
Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014). Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Transactions on Multimedia, 16(8), 2203–2213.
Newcombe, R. G. (1998). Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine, 17(8), 857–872.
Rabiner, L., & Juang, B. H. (2003). Fundamentals of Speech Recognition. Pearson Education.
Rao, C. R., & Mitra, S. K. (1971). Generalized inverse of matrices and its applications (Vol. 7). New York: Wiley.
Reynolds, D. A. (1995). Speaker identification and verification using Gaussian mixture speaker models. Speech Communication, 17, 91–108.
Reynolds, D. A., Quatieri, T. F., & Dunn, R. B. (2000). Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, 10(1–3), 19–41.
Sachdev, A., Dileep, A. D., & Thenkanidiyoor, V. (2015). Example-specific density based matching kernel for classification of varying length patterns of speech using support vector machines. In Proceedings of ICONIP, (pp. 177–184). Istanbul.
Smith, N., Gales, M., & Niranjan, M. (2001). Data-dependent kernels in SVM classification of speech patterns. Tech. Rep. CUED/F-INFENG/TR.387, Cambridge University Engineering Department, Cambridge.
Steidl, S. (2009). Automatic classification of emotion-related user states in spontaneous childern’s speech. PhD thesis, Der Technischen Fakultät der Universität Erlangen-Nürnberg, Germany.
Swain, M. J., & Ballard, D. H. (1991). Color indexing. International Journal of Computer Vision, 7(1), 11–32.
The NIST Year 2002 Speaker Recognition Evaluation Plan. (2002). http://www.itlnistgov/iad/mig/tests/spk/2002/
The NIST Year 2003 Speaker Recognition Evaluation Plan. (2003). http://www.itlnistgov/iad/mig/tests/sre/2003/
Vedaldi, A., & Zisserman, A. (2010). Efficient additive kernels via explicit feature maps. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 3539–3546).
Wang J., KYFLTH Yang, J., & Gong, Y. (2010). Locality-constrained linear coding for image classification. In Proceedings of CVPR’10, IEEE (pp. 3360–3367). State College: The Pennsylvania State University.
Yang, J., Yu, K., Gong, Y., & Huang, T. (2009). Linear spatial pyramid matching using sparse coding for image classification. In Proceedings of CVPR’09, IEEE, (pp. 1794–1801).
You, C. H., Lee, K. A., & Li, H. (2009). An SVM kernel with GMM-supervector based on the Bhattacharyya distance for speaker recognition. IEEE Signal Processing Letters, 16(1), 49–52.
Zhang, L., Zhang, D., & Tian, F. (2016). Svm and elm: Who wins? object recognition with deep convolutional features from imagenet. In Proceedings of ELM-2015 (Vol. 1, pp. 249–263). Springer: New York.
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Gupta, S., Karanath, A., Mahrifa, K. et al. Segment-level probabilistic sequence kernel and segment-level pyramid match kernel based extreme learning machine for classification of varying length patterns of speech. Int J Speech Technol 22, 231–249 (2019). https://doi.org/10.1007/s10772-018-09587-1
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DOI: https://doi.org/10.1007/s10772-018-09587-1