Direct posterior confidence for out-of-vocabulary spoken term detection
ACM Transactions on Information Systems (TOIS)
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In isolated-word recognition from everyday speech, a considerable share of the input lies outside the permitted vocabulary, and has to be rejected. The authors trained multilayer perceptrons to confirm or reject the choice made by a Markov model system during recognition by classifying the trace of the winning model. This rejection method is totally independent of the recognition procedure. Results show that performance on a database containing field data is better than with other rejection procedures.