Experiments with linear feature extraction in speech recognition
Klaus Beulen, Lutz Welling, Hermann Ney · 1995
In this paper we investigate Linear Discriminant Analysis (LDA) for the TI connected digit recognition task (TI task) and the Wall Street Journal large vocabulary recognition task (WSJ task). In addition to previous variants of LDA implementations, we avoided the explicit incorporation of derivatives in the acoustic vector. Instead a sliding window without derivatives was used. This large-sized vector was then taken to extract the features by an LDA transformation. Tests for this feature generation were performed both for Laplacian and Gaussian densities. 1. INTRODUCTION It is a well known fact that the performance of a pattern recognition system depends heavily on the type of observations that are used in the system. Several methods are employed in practice which often consist of two stages: First the acoustic signal is transformed from time domain into frequency domain using a Fourier transformation or the like. Second the spectral components of the resulting acoustic vector are th...