This work presents several approaches for extracting invariant features for automatic speech recognition systems. The robustness of these features under various training-test conditions is evaluated and it is described how the robustness of the features to noise can be increased. Furthermore, it is shown how the spectral effects due to different vocal tract lengths can be estimated with a registration method and how this can be used for speaker normalization.
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