Rejection based on a posteriori probability estimated by MLP with application for Mandarin voice dialer on ASIC

High performance Mandarin voice dialer is much more difficult than its English counterpart to achieve, especially on inexpensive hardware as ASIC. One way to improve its performance is to incorporate rejecters into the system. In our study, an MLP based postprocessor, an a posteriori probability estimator, is applied after HMM Viterbi recognition. Poor utterances, which are recognized by HMMs but have low a posteriori probability, are then rejected. Rejecting 4.9% of all the testing utterances, the MLP rejector boosts the HMM-based system´s single digit accuracy from 97.1% to 99.6% for the Mandarin voice dialer, a ten-syllable speaker independent task. The performance is better than those of rejection based on linear discrimination, anti-digit models or likelihood ratio