On data-derived temporal processing in speech feature extraction
Michael L. Shire, Barry Y. Chen · 2000
Temporal processing and filtering in speech feature extraction are commonly used to aid in performance and robustness in au-tomatic speech recognition. Among the techniques successfully employed are RASTA filtering, delta calculation, and cepstral mean subtraction. The work here explores the use of temporal filter design using LDA to further enhance performance using a few preprocessing configurations. In addition to RASTA fil-tering, we apply the filters to modulation-spectral features and cepstra while making sure that the assumptions of LDA are ob-served. We additionally test the use of filters that have been trained in different reverberation conditions, noting from previ-ous work that the presence of reverberation alters the preferred frequency range of the derived filters. Our tests indicate a con-sistent advantage in phone classification. Word recognition tests, in contrast, reveal that the LDA filters often do not improve upon the existing filters previously used. They can also be made less effectual by allowing contextual frames to a trained probability estimator. 1.