In this work we combine a conventional phone-based automatic speech recognizer with a classifier that detects syllable locations. This is done using a dynamic Bayesian network. Using oracle syllable detections we achieve a 17% relative reduction in word error rate on the 500 word task of the SVitchboard corpus. Using estimated locations we achieve a 2.1% relative reduction which is significant at the 0.02 level. The improvement in the estimated case is from reducing insertions caused by burst noise.
Bibliographic reference. Bartels, Chris D. / Bilmes, Jeff A. (2008): "Using syllable nuclei locations to improve automatic speech recognition in the presence of burst noise", In INTERSPEECH-2008, 2406-2409.