Feature decorrelation methods in speech recognition. A comparative study
Document typeConference report
PublisherInternational Speech Communication Association (ISCA)
Rights accessOpen Access
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 point 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 coeficients. 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 coeficients.
CitationBatlle, E., Nadeu, C., Fonollosa, J. A. R. Feature decorrelation methods in speech recognition. A comparative study. A: International Conference on Spoken Language Processing. "ICSLP 98: the 5th International Conference on Spoken Language Processing; incorporating the 7th Australian International Speech Science and Technology Conference; Sydney Convention Centre, Sydney, Australia, 30th November-4th December 1998". Baixas: International Speech Communication Association (ISCA), 1998.