A rejection model based on multi-layer perceptrons for Mandarin digit recognition
High performance Mandarin digit recognition (MDR) is much more difficult to achieve than its English counterpart, especially on inexpensive hardware implementation. In this paper, a new Multi-Layer Perceptrons (MLP) based postprocessor, an a posteriori probability estimator, is presented and used for the rejection model of the speaker independent Mandarin digit recognition system based on hidden Markov model (HMM). Poor utterances, which are recognized by HMMs but have low a posteriori probability, will be rejected. After rejecting about 4.9% of the tested utterances, the MLP rejection model can boost the digit recognition accuracy from 97.1% to 99.6%. The performance is better than those rejection models based on linear discrimination, likelihood ratio or anti-digit.
rejection model multi-layer perceptrons mandarin digit recognition speech recognition