CAJ | 학술논문

在手语识别研究中,非特定人手语识别参数训练的样本缺乏影响了非特定人手语识别的识别率.区分性训练可以很好的弥补由于训练样本的缺乏对识别系统所造成的影响,能够提高非特定人手语识别的识别率.对区分性训练(DT)所改进的HMM参数训练模型(DT/HMM)做了全新的推导,获得了与HMM相一致齐全的DT/HMM的参数模型.在特定人识别系统上应用可区分性训练的h准则获取了h参数,将该齐全的DT/HMM的参数训练模型和h参数,应用于大词汇量的非特定人手语识别当中,加入主观经验后的非注册易混词集EXP 与MLE和EBW的非注册易混词集相比,平均识别率分别提高了10.65%和9.55%.
재수어식별연구중,비특정인수어식별삼수훈련적양본결핍영향료비특정인수어식별적식별솔.구분성훈련가이흔호적미보유우훈련양본적결핍대식별계통소조성적영향,능구제고비특정인수어식별적식별솔.대구분성훈련(DT)소개진적HMM삼수훈련모형(DT/HMM)주료전신적추도,획득료여HMM상일치제전적DT/HMM적삼수모형.재특정인식별계통상응용가구분성훈련적h준칙획취료h삼수,장해제전적DT/HMM적삼수훈련모형화h삼수,응용우대사회량적비특정인수어식별당중,가입주관경험후적비주책역혼사집EXP 여MLE화EBW적비주책역혼사집상비,평균식별솔분별제고료10.65%화9.55%.
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 rcriterion 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 signerindependent 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.