Forward-decoding kernel-based phone sequence recognition

Shantanu Chakrabartty, Gert Cauwenberghs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Forward decoding kernel machines (FDKM) combine large-margin classifiers with hidden Markov models (HMM) for maximum a posteriori (MAP) adaptive sequence estimation. State transitions in the sequence are conditioned on observed data using a kernel-based probability model trained with a recursive scheme that deals effectively with noisy and partially labeled data. Training over very large datasets is accomplished using a sparse probabilistic support vector machine (SVM) model based on quadratic entropy, and an on-line stochastic steepest descent algorithm. For speaker-independent continuous phone recognition, FDKM trained over 177,080 samples of the TIMET database achieves 80.6% recognition accuracy over the full test set, without use of a prior phonetic language model.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
StatePublished - 2003
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: Dec 9 2002Dec 14 2002

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference16th Annual Neural Information Processing Systems Conference, NIPS 2002
CityVancouver, BC


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