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A training algorithm for statistical sequence recognition with applications to transition-based speech recognition

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3 Author(s)
H. Bourlard ; Int. Comput. Sci. Inst., Berkeley, CA, USA ; Y. Konig ; N. Morgan

In this letter, we introduce a discriminant training algorithm for statistical sequence recognition that uses a transition-based stochastic finite state automaton with posterior transition probabilities conditioned on the current input observation and the previous state. This provides a framework for frame-synchronous speech recognition in which posterior probabilities are estimated as the basis for recognition, rather than the state-dependent probability densities that are conventionally used. Preliminary speech recognition experiments support the theory by showing an increase in the estimates of posterior probabilities of the correct sentences and a statistically significant decrease in error rates for independent test sets.

Published in:

IEEE Signal Processing Letters  (Volume:3 ,  Issue: 7 )