Improving and applying a signer-independent sign language recognition parameter training model

In sign language recognition, a lack of training samples for signer-independent sign language decreases recognition rates due to an inability to identify suitable parameters. Discriminative training methods can improve the impact of insufficient training samples on the recognition system while increasing the recognition rate of signer-independent sign language recognition (SISLR). Hidden Markov model (HMM) and dependency-tree hidden Markov model (DT-HMM) improvements through discriminative training were proven theoretically possible, so a DT-HMM model with complete parameters was derived and proven to be consistent with the HMM model. We obtained the h parameter by applying the h criterion of discriminative training to recognition systems optimized for specific people. A full range of DT-HMM parameter model consistent with HMM has been deduced in this paper. The h parameters are worked out by applying the h criterion of Discriminative Training method to a Signer-dependent Sign Language Recognition. Then, applying the full range of DT-HMM parameter model in a large vocabulary of words for signer-independent sign language recognition(SISLR), to EXP, the average rates of recognition increase 10.65% and 9.55% compare with the nonregistered confusable set of MLE and EBW respectively.