In this paper we study various decorrelation methods for the features used in speech recognition and we compare the performance of each one by running several tests with a speech database. First of all we study the Principal Components Analysis (PCA). PCA extracts the dimensions along which the data vary the most, and thus it allows us to reduce the dimension of the data points without significant loss of performance. The second transform we study is the Discrete Cosine Transform (DCT). As it will be shown, it is an approximation of the PCA analysis. By applying this transform to FBE parameters we obtain the MFCC coefficients. A further step is taken with the Linear Discriminant Analysis (LDA), which, not only reduces the dimensionality of the problem, but also discriminates among classes to reduce the confusion error. The last method we study is Frequency Filtering (FF). This method consists of a linear filtering of the frequency sequence of the log FBE that both decorrelates and equalizes the variance of the coefficients.
Cite as: Batlle, E., Nadeu, C., Fonollosa, J.A.R. (1998) Feature decorrelation methods in speech recognition. a comparative study. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0473, doi: 10.21437/ICSLP.1998-511
@inproceedings{batlle98_icslp,
author={Eloi Batlle and Climent Nadeu and José A.R. Fonollosa},
title={{Feature decorrelation methods in speech recognition. a comparative study}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0473},
doi={10.21437/ICSLP.1998-511}
}