Abstract
The definition of a phoneme as a fuzzy set of minimal speech units from the model database is proposed. On the basis of this definition and the Kullback-Leibler minimum information discrimination principle the novel phoneme recognition algorithm has been developed as an enhancement of the phonetic decoding method. The experimental results in the problems of isolated vowels recognition and word recognition in Russian are presented. It is shown that the proposed method is characterized by the increase of recognition accuracy and reliability in comparison with the phonetic decoding method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Benesty, J., Sondh, M., Huang, Y. (eds.): Springer Handbook of Speech Recognition. Springer (2008)
Ramirez, J., Segura, J.C., Benitez, C., de la Torre, A., Rubio, A.J.: A new Kullback-Leibler VAD for speech recognition in noise. IEEE Signal Processing Letters 11(2), 266–269 (2004)
Gruhn, R., Raab, M., Brueckner, R.: US Patent №8301445, Speech Recognition Based on a Multilingual Acoustic Model. Nuance Communications, Inc., Assignee (2012)
Kullback, S.: Information Theory and Statistics. Dover Pub. (1997)
Savchenko, V.V.: The Method of Words Phonetic Decoding in Automatic Speech Recognition Problem Using the Minimum Information Discrimination Principle, Izvestia vuzov Rossii. Radioelectronika 5, 31–41 (2009) (in Russian)
Qiao, Y., Shimomura, N., Minematsu, N.: Unsupervised optimal phoneme segmentation: Objectives, algorithm and comparisons. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3989–3992 (2008)
Rasipuram, R., Magimai-Doss, M.: Improving Articulatory Feature and Phoneme Recognition Using Multitask Learning. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 299–306. Springer, Heidelberg (2011)
Zadeh, L.A.: Fuzzy Sets. Information Control 8, 338–353 (1965)
Marple -Jr., S.L.: Digital Spectral Analysis: With Applications. Prentice-Hall Series in Signal Processing (1989)
Hill, J.E.: The Minimum of n Independent Normal Distributions, http://www.untruth.org/~josh/math/normal-min.pdf
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press (1981)
Koutroumbas, K., Theodoridis, S.: Pattern Recognition, 4th edn. Elsevier Inc. (2008)
Ronzhin, A.L., Yusupov, R.M., Li, I.V., Leontieva, A.B.: Survey of Russian Speech Recognition Systems. In: SPECOM 2006, pp. 54–60 (2006)
Reddy, D.R.: Speech recognition by Machine: A Review. Proceedings of the IEEE 64(4), 501–531 (1976)
Jensen, R., Cornelis, C.: Fuzzy-rough nearest neighbour classification and prediction. Theoretical Computer Science 412(42), 5871–5884 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Savchenko, L.V., Savchenko, A.V. (2013). Fuzzy Phonetic Decoding Method in a Phoneme Recognition Problem. In: Drugman, T., Dutoit, T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science(), vol 7911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38847-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-38847-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38846-0
Online ISBN: 978-3-642-38847-7
eBook Packages: Computer ScienceComputer Science (R0)