A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition

T. Eisele, R. Haeb-Umbach, D. Langmann, in: ICSLP , Philadelphia, 1996.

Conference Paper | English
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Although widely used, there are still open questions concerning which properties of linear discriminant analysis (LDA) account for its success in many speech recognition systems. In order to gain more insight into the nature of the transformation we compare LDA with mel-cepstral feature vectors with respect to the following criteria: decorrelation and ordering property; invariance under linear transforms; automatic learning of dynamical features; and data dependence of the transformation.
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ICSLP , Philadelphia
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Eisele T, Haeb-Umbach R, Langmann D. A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition. In: ICSLP , Philadelphia. ; 1996.
Eisele, T., Haeb-Umbach, R., & Langmann, D. (1996). A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition. In ICSLP , Philadelphia.
@inproceedings{Eisele_Haeb-Umbach_Langmann_1996, title={A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition}, booktitle={ICSLP , Philadelphia}, author={Eisele, Thomas and Haeb-Umbach, Reinhold and Langmann, Detlev}, year={1996} }
Eisele, Thomas, Reinhold Haeb-Umbach, and Detlev Langmann. “A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition.” In ICSLP , Philadelphia, 1996.
T. Eisele, R. Haeb-Umbach, and D. Langmann, “A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition,” in ICSLP , Philadelphia, 1996.
Eisele, Thomas, et al. “A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition.” ICSLP , Philadelphia, 1996.

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