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Author Zhang, Bing ♦ Matsoukas, Spyros ♦ Schwartz, Richard
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description 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.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution in Proceedings of INTERSPEECH, 2006