A Comparative Study Of Linear Feature Transformation Techniques For Automatic Speech Recognition (1996)
| Venue: | in Proc. Int. Conf. on Spoken Language Processing |
| Citations: | 10 - 1 self |
BibTeX
@INPROCEEDINGS{Eisele96acomparative,
author = {Thomas Eisele and Reinhold Haeb-Umbach and Detlev Langmann and Philips Gmbh Forschungslaboratorien},
title = {A Comparative Study Of Linear Feature Transformation Techniques For Automatic Speech Recognition},
booktitle = {in Proc. Int. Conf. on Spoken Language Processing},
year = {1996},
pages = {252--255}
}
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Abstract
Although widely used, there are still open questions concerning which properties of Linear Discriminant Analysis (LDA) do 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.
Citations
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| 550 | Comparison of parametric representations of monosyllabic word recognition in continuously spoken sentences - Davis, Mermelstein - 1980 |
| 54 | Linear discriminant analysis for improved large vocabulary continuous speech recognition - Haeb-Umbach, Ney - 1992 |
| 43 | A comparison of several acoustic representations for speech recognition with degraded and undegraded speech - Hunt, Lefebvre - 1989 |
| 9 | On the Robustness of Linear Discriminant Analysis as a Preprocessing Step for Noisy Speech Recognition - Siohan - 1995 |
| 9 | Continuous Mixture Densities and Linear Discriminant Analysis for Improved Context-Dependent Acoustic Models - Aubert, Haeb-Umbach, et al. - 1993 |
| 8 | Experiments with linear feature extraction in speech recognition - Beulen, Welling, et al. - 1995 |
