Rejection of extraneous input in speech recognition applications, using multi-layer perceptrons and the trace of HMMs

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