Dimensionality reduction of dynamical patterns using a neural network
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 256-265
- https://doi.org/10.1109/nnsp.1991.239516
Abstract
To recognize speech with dynamical features, one should use feature parameters including dynamical changing patterns, that is, time sequential patterns. The K-L expansion has been used to reduce the dimensionality of time sequential patterns. This method changes the axes of feature parameter space linearly by minimizing the error between original and reconstructed parameters. In this paper, the dimensionality of dynamical features is reduced by using one nonlinear dimensional compressing ability of the neural network. The authors compared the proposed method on speech recognition using a continuous HMM (hidden Markov model) with the reduction method using one K-L expansion and the feature parameters of regression coefficients in addition to original static features.<>Keywords
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