This paper presents the nonlinear speech phoneme decomposition based on Volterra-Wiener functional series. It is shown the usage this nonlinear decomposition in speech recognition systems constructing. The fast algorithms for finding estimation of Wiener kernels in frequency domain permit to reduce essentially computing expenses for evaluation of signals decomposition.
Index Terms. speech recognition, nonlinear signal decomposition, Volterra-Wiener functional series, Wiener kernels measuring, fast algorithms
Cite as: Krot, A.M., Tkachova, P.P. (2001) Fast algorithms for Wiener kernels computing in speech phoneme recognition. Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001), 246-253
@inproceedings{krot01c_maveba,
author={Alexander M. Krot and Polina P. Tkachova},
title={{Fast algorithms for Wiener kernels computing in speech phoneme recognition}},
year=2001,
booktitle={Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001)},
pages={246--253}
}