Recent progress on the discriminative region-dependent transform for speech feature extraction
Bing Zhang, Spyros Matsoukas, Richard M. Schwartz · 2006
The region-dependent transform (RDT) is a feature extrac-tion method for speech recognition that employs the Minimum Phoneme Error (MPE) criterion to optimize a set of feature trans-forms, each concentrating on a region of the acoustic space. Pre-vious results have shown that RDT gives significant recognition-error reduction in a large vocabulary speaker-independent (SI) sys-tem. As a follow-up investigation, this paper presents the re-cent progress of applying RDT in speaker-adaptive training (SAT). Similar to previous SI results, the integration of RDT with SAT yields 7 % relative improvement in word error rate (WER). Also, theoretical comparisons are made between RDT and other discrim-inative feature extraction methods, including the improved version of the feature-space MPE (fMPE) that uses the “mean-offsets ” as additional input features. Index Terms: speech recognition, discriminative training, feature extraction, region-dependent transform.