Fast Discriminant Training of Semi-continuous HMM

In this paper, we introduce a fast estimate algorithm for discriminant training of semi-continuous HMM (Hidden Markov Models). We first present the Frame Discrimination (FD) method proposed in [9] for weight re-estimate. Then, the weight update equation is formulated in the specific framework of semi-continuous models. Finally, we propose an approximated update function which requires a very low level of computational resources. The first experiments validate this method by comparing our fast discriminant weighting (FDW) to the original one. We observe that, on a digit recognition task, FDW and FD estimate obtain similar results, when our method decreases significantly the computational time. A second experiment evaluates FDW in Large Vocabulary Continuous Speech Recognition (LVCSR) task. We incorporate semi-continuous FDW models in a Broadcast News (BN) transcription system. Experiments are carried out in the framework of ESTER evaluation campaign ([2]). Results show that in particular context of very compact acoustic models, discriminant weights improve the system performance compared to both a baseline continuous system and a SCHMM trained by MLE algorithm.