Feature decorrelation methods in speech recognition. A comparative study
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Cita com:
hdl:2117/117385
Tipus de documentText en actes de congrés
Data publicació1998
EditorInternational Speech Communication Association (ISCA)
Condicions d'accésAccés obert
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Abstract
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.
CitacióBatlle, 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.
ISBN1-876346-17- 5
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