Tone modeling based on hidden conditional random fields and discriminative model weight training

The use of hidden conditional random fields (HCRFs) for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training (DMWT) is proposed. Acoustic and tone scores are scaled by model weights discrimina-tively trained by the minimum phone error (MPE) criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition (LVCSR) can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.