Dimensionality reduction of dynamical patterns using a neural network

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