Abstract
Currently, various time-frequency representations are often used for sound analysis. These representations, on the one hand, are convenient for visible sensation of sound by a human and, on the other hand, can be used for automatically analyzing sound pictures. In this paper, various methods for representation of sound as two-dimensional time-frequency vectors of a fixed dimension and their use for speech and speaker recognition problems are discussed. Probabilistic, distance-based, and neural-network methods for the recognition of these vectors by examples of separate words are considered. Numerical experiments showed that the best among them is the method based on a three-layer neural network, the short-time Fourier transform, and the two-dimensional wavelet transformation. For the speaker recognition problem, a distance-based recognition method employing the adaptive Hermite transform turned out the best among all.
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REFERENCES
Bourland, H. and Morgan, N., Hybrid HMM/ANN Systems for Speech Recognition: Overview and New Research Directions, http://www.tzi.org/ik98/prog/kursunterlagen/-t2/bourland.html.
Tran, D., Wagner, M., and Zheng, T., A Fuzzy Approach to Statistical Models in Speech and Speaker Recognition, Proc. of 1999 IEEE Int. Fuzzy Systems Conf., Korea, pp. 1275-1280.
Lippman, R. and Gold, B., Neural Classifiers Useful for Speech Recognition, Proc. IEEE First Int. Conf. on Neural Networks, 1987, vol. 4, pp. 417-422.
Gold, B. and Morgan, N., Speech and Audio Signal Processing, Wiley, 1999.
Demars, C., Two-Dimensional Representations of Speech Signal. Time-frequency Representation and Parametrizations, 1999, http://www.limsi.fr/Individu/-chrd/tablematniE2001.html.html.
Chan, C.P., Lee, T., and Ching, P.C., Two-Dimensional Multi-Resolution Analysis of Speech Signals and Its Application to Speech Recognition, Speech and Signal Processing (Proc. of 1999 IEEE Int. Conf. on Acoustics), 1999, vol. 1, pp. 405-408.
Dvoryankin, S., Relationship between Digits and Graphics, Sound and Image, Otkrytye sistemy, 2000, no. 3, pp. 25-32.
Szego, G., Orthogonal Polynomials, Am. Math. Soc. Colloquium Publications, 1959, vol. 23.
Jeckson, D., Fourier Series and Orthogonal Polynomials, in Carus Mathematical Monographs, 1941, no. 6.
Martens, J.-B., The Hermite Transform-Theory, IEEE Trans. Acoustics, Speech, Signal Processing, 1990, vol. 38, pp. 1595-1606
Martens, J.-B., The Hermite Transform-Applications, IEEE Trans. Acoustics, Speech, Signal Processing, 1990, vol. 38, pp. 1607-1618.
Krylov, A. and Kortchagine, D.N., Projection Filtering in Image Processing, Proc. of the Conf. Graphicon' 2000, Moscow, 2000, pp. 42-45.
Krylov, A. and Liakishev, A.V., Numerical Projection Method for Inverse Fourier Transform and Its Application, Numerical Functional Analysis Optimization, 2000, vol. 21, pp. 205-216.
Krylov, A.S., Kortchagine, D.N., and Lukin, A.S., Streaming Waveform Data Processing by Hermite Expansion for Text-Independent Speaker Indexing from Continuous Speech, Proc. of the Conf. Graphicon' 2002, Nizhni Novgorod, 2002, pp. 91-98.
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Zhirkov, A.O., Kortchagine, D.N., Lukin, A.S. et al. Graphic Representation Method and Neural Network Recognition of Time-Frequency Vectors of Speech Information. Programming and Computer Software 29, 210–218 (2003). https://doi.org/10.1023/A:1024970609361
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DOI: https://doi.org/10.1023/A:1024970609361