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Second, we research the Baum-Welch algorithm of modified HMM and derive the update parametric estimation equations for modified HMM based on traditional HMM.

其次，在传统的隐马尔可夫模型的基础上，研究了改进的模型的前向-后向算法和Baum-Welch算法，并导出了改进的模型的参数估计公式。

VQ is easy and tend to realize, HMM can comminute the speech dynamically, it can embody not only the dynamic characteristic but also the stationary characteristic of the speech. GMM is a special HMM, it uses combination of many weighted Gaussian density function to approach the distributing density of speaker characteristic vector in characteristic space, so it is also a good way.

VQ方法是最简单又易于实现，HMM方法能将语音动态分割，既能体现语音的动态特征，又能体现语音的平稳特性，GMM用多个加权高斯密度函数的组合来逼近说话人特征矢量在特征空间的分布密度，收到了较好的结果。

Up to this time, the major challenge to sign language recognition is how to develop approaches that scale well with increasing vocabulary size In this paper, an approach to large vocabulary, continuous Chinese sign language recognition is presented, which uses subwords instead of whole signs as the basic units Since the number of subwords is limited, HMM based training and recognition of the CSL signal become more tractable and have the potential to recognize enlarged vocabularies Furthermore, the proposed method facilitates the CSL recognition when finger alphabet is blended with gestures About 2400 subwords are defined for CSL One HMM is built for each subword, and then the signs are encoded based on these subwords A decoder that uses tree structured network is presented Clustering of the Gaussians on the state, language model and N best is used to improve the performance of the system Experiments on a 5119 sign vocabulary are carried out, and the correct rate is over 90% for continuous sign recognition

迄今为止，手语识别面临的最大问题是如何解决词汇集易扩充的连续识别提出一种大词汇量连续中国手语识别方法，将词根作为识别基元，由于基元的数目是有限的，因此基于HMM的手语信号的训练和识别变得比较容易处理，可以实现更大词汇量的识别除此之外，所提方法还有利于实现手势语和手指语的混合识别从中国手语中共整理出2 4 0 0多个词根，为每个词根建一个并行的HMM模型，对各数据流的HMM模型进行聚类，确定出手语识别的基元根据这些基元对手势词编码，并建立了树状搜索网格，使用状态结点上高斯密度函数聚类、语言模型和N Best方法提高系统的速度和精度对 5 119个手语词做了实验，连续语句的识别率可在 90 %以上

HMM is good at dealing with sequential inputs,while SVM shows superior performance in classification especially for limited samples.Therefore,they can be combined to get a better and effective multilayer architecture classifier.SVM is used to resolve the uncertainty of the remaining signal which is confusable after the HMM-based recognition.

针对战场环境下声信号的特点，算法综合考虑HMM适合处理连续动态信号及SVM小样本情况下的强分类能力，利用HMM处理待辨识的连续动态信号，将HMM易混淆的信号作为与待辨识信号较为相似的模式类，形成候选模式集，再由SVM在候选模式中对待辨识信号作最后决策。

This paper bases on the former documents which related to the distance in HMM (we also proved Juang and Rabine's intuitionistic discussion strictly ), and study the distance in HMM from two aspects:the one is to find the more rapid and effective approximate algorithm, the other is to introduce a new and more applied distance.

针对J-R距离在理论上很有意义，但实际上难以计算且结果具有不确定性的缺点，利用文献[11]，得到了近似计算离散HMM和具有混合高斯观测密度的连续HMM距离的一种快速有效的算法，该算法用矩阵形式来表达，便于用MATLAB进行计算。

The algorithm trains HMM models as a baseline system and uses support vector machines training scheme to rescore the parameters of HMM.

在改进方法中，首先训练连续隐马尔可夫模型参数，使用支持向量机对参数进行判别性训练，然后用训练得到的支持向量对原始模型参数重新估值，得到新的模型参数。

By using a tree pictorial structure model to represent the detuned generic people in the video, and a modified Hidden Markov Model to simulate the motion of people between the two frames of the video, a machine learning method is used to the modified HMM to obtain the estimation of parameter of the modified HMM,and to capture the people model from the video.

利用片图模型表示未经学习的人体，改进的隐马尔可夫模型模拟人体在视频序列各帧间的运动，并使用机器学习方法对该改进的HMM进行推理，获取改进HMM的参数，从而获得所需的人体模型。

Among these members, three particularly popular scanners stand out: HMM (who provides Taiwanese manga scans), C.C., and ADAD (mostly shoujo and josei manga scans).

扫港版日文漫，港漫会被误解）和ADAD（扫少女和Josei详解见下），他们们扫漫作品在美国被视为好肉

Sherry: Hmm, you're working on the interview with the airline pilot and the New York shopping article, right?

雪莉： 嗯，你正在写访问机长以及在纽约购物的文章是吗？

ALLY: Hmm, Well, I don't know.

嗯，我不知道。

Now she was hungry and angry.She began to smoulder.

现在她又饿又气，她开始流露难以抑制的怒火。

You have placed our iniquities before You, Our secret sins in the light of Your presence.

诗90：8 你将我们的罪孽摆在你面前、将我们的隐恶摆在你面光之中。