Corrective and reinforcement learning for speaker-independent continuous speech recognition
نویسندگان
چکیده
This paper addresses the issue of learning hidden Markov model (HMM) parameters for speaker-independent continuous speech recognition. Bahl et al. [Bahl 88a] introduced the corrective training algorithm for speaker-dependent isolated word recognition. Their algorithm attempted to improve the recognition accuracy on the training data. In this work, we extend this algorithm to speaker-independent continuous speech recognition. We use cross-validation to increase the effective training size. We also introduce a near-miss sentence hypothesization algorithm for continuous speech training. The combination of these two approaches resulted in over 20% error reductions both with and without grammar. This research was sponsored by Defense Advanced Research Projects Agency Contract N00039-85-C-0163. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies either expressed or implied, of the Defense Advanced Research Projects Agency, or teUSGov«mmaT^ ^ Table of
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