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Título:
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Feature decorrelation methods in speech recognition. A comparative study
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Autor/a:
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Batlle Mont, Eloi; Nadeu Camprubí, Climent; Rodríguez Fonollosa, José Adrián
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
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Abstract:
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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. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
-Automatic speech recognition
-Reconeixement automàtic de la parla |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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Editor:
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International Speech Communication Association (ISCA)
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Compartir:
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