Abstract
In this paper we introduce three ideas for phoneme classification: First, we derive the necessary steps to integrate linear transforms into the computation of reproducing kernels. This concept is not restricted to phoneme classification and can be applied to a wider range of research subjects. Second, in the context of support vector machine (SVM) classification, correlation features based on MFCC-vectors are proposed as a substitute for the common first and second derivatives, and the theory of the first part is applied to the new features. Third, an SVM structure in the spirit of phoneme states is introduced. Relative classification improvements of 40.67% compared to stacked MFCC features of equal dimension encourage further research in this direction.
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References
Rodríguez, E., et al.: Speech/Speaker Recognition Using a HMM/GMM Hybrid Model. LNCS, vol. 1206, pp. 227–234. Springer, Heidelberg (1997)
Chakrabartty, S., Cauwenberghs, G.: Forward-Decoding Kernel-Based Phone Sequence Recognition. Adv. Neural Information Processing Systems (2002)
Smith, N., Gales, M.J.F.: Using SVMs and Discriminative Models for Speech Recognition. In: IEEE International Conference on Accoustic Speech and Signal Processing, vol. 1, pp. 77–80 (2002)
Campbell, W.M.: A Sequence Kernel and its Application to Speaker Recognition. In: Neural Information Processing Systems, vol. 14, pp. 1157–1163 (2001)
Shawe-Taylor, J., Chrstianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Lanckriet, G.R.G., et al.: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning 5, 27–72 (2004)
Lanckriet, G.R.G., et al.: A Statistical Framework for Genomic Data Fusion. Bioinformatics 20, 2626–2635 (2004)
Hirokai, T., et al.: Simple but Effective Methods for Combining Kernels in Computational Biology. RIVF, 71–78 (2008)
Aronszajn, N.: Theory of Reproducing Kernels. Transactions of the American Mathematical Society 68, 337–404 (1950)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Agler, J., McCarthy, K.: Pick Interpolation and Hilbert Functions Spaces. Graduate Studies in Mathematics, vol. 44. American Mathematical Society, Providence (2002)
Bennet, K., Magasarian, O.: Robust Linear Programming Discriminaion of Two Linearly Inseparable Sets. Optimization Methods and Software 1, 22–34 (1992)
Cortes, C., Vapnik, V.: Support Vector Machines. Machine Learning 20, 273–297 (1995)
Shigoe, A.: Support Vector Machines for Pattern Classification. In: Advances in Pattern Recognition, Springer, Heidelberg (2005)
Joachims, T.: Making Large-Scale SVM Learning Practical. Advances in Kernel Methods – Support Vector Learning (1999)
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Beschorner, A., Klakow, D. (2010). Correlation Features and a Linear Transform Specific Reproducing Kernel. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_30
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DOI: https://doi.org/10.1007/978-3-642-15760-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15759-2
Online ISBN: 978-3-642-15760-8
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