@inproceedings{11869, abstract = {{Amongst several data driven approaches for designing filters for the time sequence of spectral parameters, the linear discriminant analysis (LDA) based method has been proposed for automatic speech recognition. Here we apply LDA-based filter design to cepstral features, which better match the inherent assumption of this method that feature vector components are uncorrelated. Extensive recognition experiments have been conducted both on the standard TIMIT phone recognition task and on a proprietary 130-words command word task under various adverse environmental conditions, including reverberant data with real-life room impulse responses and data processed by acoustic echo cancellation algorithms. Significant error rate reductions have been achieved when applying the novel long-range feature filters compared to standard approaches employing cepstral mean normalization and delta and delta-delta features, in particular when facing acoustic echo cancellation scenarios and room reverberation. For example, the phone accuracy on reverberated TIMIT data could be increased from 50.7\% to 56.0\%}}, author = {{Lieb, M. and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2000)}}, keywords = {{acoustic echo cancellation algorithms, adverse environmental conditions, automatic speech recognition, cepstral analysis, cepstral features, cepstral mean normalization, command word task, delta-delta features, delta features, echo suppression, error rate reductions, feature vector components, FIR filters, LDA derived cepstral trajectory filters, linear discriminant analysis, long-range feature filters, phone accuracy, real-life room impulse responses, reverberant data, spectral parameters, speech recognition, standard TIMIT phone recognition task}}, pages = {{II1105--II1108 vol.2}}, title = {{{LDA derived cepstral trajectory filters in adverse environmental conditions}}}, doi = {{10.1109/ICASSP.2000.859157}}, volume = {{2}}, year = {{2000}}, }