We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achie...We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achieve this,we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively.The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization(EM) like and the formation of the updated result is similar to that obtained with the confusion network(CN) decoding method.Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations,compared with CN decoding,confusion network combination(CNC),and ROVER methods.展开更多
最近混淆网络在融合多个机器翻译结果中展示很好的性能.然而为了克服在不同的翻译系统中不同的词序,假设对齐在混淆网络的构建上仍然是一个重要的问题.但以往的对齐方法都没有考虑到语义信息.本文为了更好地改进系统融合的性能,提出了...最近混淆网络在融合多个机器翻译结果中展示很好的性能.然而为了克服在不同的翻译系统中不同的词序,假设对齐在混淆网络的构建上仍然是一个重要的问题.但以往的对齐方法都没有考虑到语义信息.本文为了更好地改进系统融合的性能,提出了用词义消歧(Word sense disambiguation,WSD)来指导混淆网络中的对齐.同时骨架翻译的选择也是通过计算句子间的相似度来获得的,句子的相似性计算使用了二分图的最大匹配算法.为了使得基于WordNet词义消歧方法融入到系统中,本文将翻译错误率(Translation error rate,TER)算法进行了改进,实验结果显示本方法的性能好于经典的TER算法的性能.展开更多
Bayes risk (BR) decoding methods have been widely investigated in the speech recognition area due to its flexibility and complexity compared with the maximum a posteriori (MAP) method regarding to minimum word error (...Bayes risk (BR) decoding methods have been widely investigated in the speech recognition area due to its flexibility and complexity compared with the maximum a posteriori (MAP) method regarding to minimum word error (MWE) optimization. This paper investigates two improved approaches to the BR decoding, aiming at minimizing word error. The novelty of the proposed methods is shown in the explicit optimization of the objective function, the value of which is calculated by an improved forward algorithm on the lattice. However, the result of the first method is obtained by an expectation maximization (EM) like iteration, while the result of the second one is achieved by traversing the confusion network (CN), both of which lead to an optimized objective function value with distinct approaches. Experimental results indicate that the proposed methods result in an error reduction for lattice rescoring, compared with the traditional CN method for lattice rescoring.展开更多
文摘We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achieve this,we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively.The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization(EM) like and the formation of the updated result is similar to that obtained with the confusion network(CN) decoding method.Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations,compared with CN decoding,confusion network combination(CNC),and ROVER methods.
文摘最近混淆网络在融合多个机器翻译结果中展示很好的性能.然而为了克服在不同的翻译系统中不同的词序,假设对齐在混淆网络的构建上仍然是一个重要的问题.但以往的对齐方法都没有考虑到语义信息.本文为了更好地改进系统融合的性能,提出了用词义消歧(Word sense disambiguation,WSD)来指导混淆网络中的对齐.同时骨架翻译的选择也是通过计算句子间的相似度来获得的,句子的相似性计算使用了二分图的最大匹配算法.为了使得基于WordNet词义消歧方法融入到系统中,本文将翻译错误率(Translation error rate,TER)算法进行了改进,实验结果显示本方法的性能好于经典的TER算法的性能.
文摘Bayes risk (BR) decoding methods have been widely investigated in the speech recognition area due to its flexibility and complexity compared with the maximum a posteriori (MAP) method regarding to minimum word error (MWE) optimization. This paper investigates two improved approaches to the BR decoding, aiming at minimizing word error. The novelty of the proposed methods is shown in the explicit optimization of the objective function, the value of which is calculated by an improved forward algorithm on the lattice. However, the result of the first method is obtained by an expectation maximization (EM) like iteration, while the result of the second one is achieved by traversing the confusion network (CN), both of which lead to an optimized objective function value with distinct approaches. Experimental results indicate that the proposed methods result in an error reduction for lattice rescoring, compared with the traditional CN method for lattice rescoring.
