延时HMM在基因剪接供体位点识别中的应用

为使得隐马尔可夫模型(HMM)能够处理非相邻可见符号之间的依赖关系,将延时机制引入标准的HMM中。该技术仅仅改变了高阶状态发射概率的计算。所有适用于HMM的算法基本保持不变。该文设计了一个一阶延时隐马尔可夫模型和一个一阶标准隐马尔可夫模型,将两者分别应用于水稻基因剪接供体位点的识别。识别结果显示,延时模型的判别能力在一定程度上优于标准模型。对那些特征很不符合的位点,延时模型给出了相对低得多的得分。     

基于多分辨率的多层分类器的手语识别方法

本文提出了一种基于多分辨率的多层分类器的手语识别方法,该方法对来自数据手套的手语输入,先用多分辨率选择特征,然后根据这些特征数据先进行低分辨率识别,再使用全部数据进行高分辨率识别。实验结果表明,该方法比传统HMM(隐马尔可夫模型)识别过程识别速度平均提高了约0.6秒,识别率提高了6.73％。     

一种基于DTW/ISODATA算法的多层分类器手语识别方法

为提高大词汇量手语识别速度,论文提出了一种将动态时间规整(DTW)和隐马尔可夫模型(HMM)相结合的多层次的大词汇量手语识别方法。该方法思想是先进行全局粗略搜索,将要识别的手势词归入某一组范围较小的词表中,然后通过更加精确的HMM局部搜索将词识别出来。各个词汇表用DTW/ISODATA算法来产生。对4942个孤立手语词作了实验,结果表明,相对于仅用HMM单层识别而言,识别速度从原来每个词的2.364秒提高到0.137秒,提高了94.2%,识别准确率也提高了4.66%。     

基于SOFM/HMM模型的非特定人手语识别系统

手语识别是通过计算机提供一种有效而准确的机制将手语翻译成文本或语音。目前最新发展水平的手语识别系统在实际应用中应该解决非特定人手语识别问题。该文在分析非特定人手语识别特点－数据多且差异大、模型训练难收敛、对不同人数据的特征提取需求更迫切－的基础上，提出了SOFM／HMM模型，将自组织特征映射（SOFM）很强的特征提取功能和隐马可夫模型（HMM）良好的处理时间序列属性结合在一个新颖的框架下，并把该模型应用到非特定人中国手语识别中，实验结果表明，SOFM／HMM模型手语识别率比传统的HMM模型提高近5％。     

基于状态持续时间的HMM语音识别模型

针对经典隐马尔可夫模型对状态持续时间的函数表达与实际语音的物理事实不相符合这一缺点,在通常隐马尔可夫的基础上引入状态持续时间参数,建立基于状态持续时间的HMM语音识别模型（SDHMM）,并用其进行语音识别实验,与经典隐马尔可夫模型相比,识别率有所提高。     

基于Improved-HMM的进程行为异常检测

针对传统隐马尔可夫模型(HMM)状态转移概率仅与前一状态有关的不足,提出了一种改进的隐马尔可夫模型(Im-proved-HMM),该模型考虑到状态转移概率与前两时刻状态相关,旨在提高异常检测准确率。用基于Improved-HMM的Baum-Welch(BW)算法对正常进程行为进行建模,并采用滑动窗口的方法,检测进程行为是否处于异常状态。实验结果表明,该模型的检测准确率高于传统的HMM模型,能及时、准确检测到进程行为的异常。     

基于一种改进禁忌搜索算法优化离散隐马尔可夫模型

隐马尔可夫模型(HMM,HiddenMarkovModel)是语音识别和手势识别中广泛使用的统计模式识别方法。文章提出了一种改进的禁忌搜索(ITS,ImprovedTabuSearch)优化HMM的参数。传统的TabuSearch(TS)与局部搜索算法(极大似然法)交替进行,从而加快了算法的收敛速度,并得到优化解。分别用TS及ITS训练隐马尔可夫模型进行动态手势识别。结果表明ITS可获得更高的识别率,且能达到全局优化。     

基于隐马尔可夫模型的自动人脸识别方法

基于DCT系数的伪二维隐马尔可夫人脸模型(P2D HMM)由于更好的利用了人脸图像 的二维统计特性,所以与基于KLT系数的一维隐马尔可夫人脸模型相比,识别效果更好,但是结构复 杂、运算量大。一维隐马尔可夫模型(1D HMM)表现二维人脸存在不足,但训练识别比较简单。综合 考虑二者的优缺点,结合支持向量机(SVM)对静态数据识别效率明显的长处,建立了SVM和HMM 的混合人脸识别模型。采用独立分量分析(ICA)的方法提取人脸区域的特征,作为SVM的输入。在 ORL人脸库中,测试基于SVM/HMM的人脸识别方法,实验结果表明该方法在获得与P2D HMM相应 的识别率的前提下,结构简单,运算量小。     

基于Leap Motion的手语识别算法优化

Leap Motion设备产生的数据在虚拟环境中可以进行手势识别。通过识别和跟踪用户的手来生成虚拟3D手部模型,从而获取手势信息。本文设计了一种基于隐马尔可夫模型(Hidden Markov Model, HMM)分类算法来学习从Leap Motion中所获取的手势信息的系统,根据手势特征的重要性赋予不同权值,可进一步提高分类准确率,实现手语信息的识别输入。测试结果表明,识别准确率为86.1%,手语打字输入识别速度为每分钟13.09个字符,可显著提高聋哑人与正常人之间沟通的便捷性。     

基于循环神经网络的语音识别模型

近年来基于隐马尔可夫模型（HMM）的语音识别技术得到了很大发展。然而HMM模型有着一定的局限性,如何克服HMM的一阶假设和独立性假设带来的问题一直是研究讨论的热点,在语音识别中引入神经网络的方法是克服HMM局限性的一条途径。该文将循环神经网络应用于汉语语音识别,修改了原网络模型并提出了相应的训练方法,实验结果表明该模型具有良好的连续信号处理性能,与传统的HMM模型效果相当,新的训练策略能够在提高训练速度的同时,使得模型分类性能有明显提高。     

A Chinese sign language recognition system based on SOFM/SRN/HMM

In sign language recognition (SLR), the major challenges now are developing methods that solve signer-independent continuous sign problems. In this paper, SOFM/HMM is first presented for modeling signer-independent isolated signs. The proposed method uses the self-organizing feature maps (SOFM) as different signers' feature extractor for continuous hidden Markov models (HMM) so as to transform input signs into significant and low-dimensional representations that can be well modeled by the emission probabilities of HMM. Based on these isolated sign models, a SOFM/SRN/HMM model is then proposed for signer-independent continuous SLR. This model applies the improved simple recurrent network (SRN) to segment continuous sign language in terms of transformed SOFM representations, and the outputs of SRN are taken as the HMM states in which the lattice Viterbi algorithm is employed to search the best matched word sequence. Experimental results demonstrate that the proposed system has better performance compared with conventional HMM system and obtains a word recognition rate of 82.9% over a 5113-sign vocabulary and an accuracy of 86.3% for signer-independent continuous SLR.     

基于SRN/HMM的非特定人连续手语识别系统

手语识别是通过计算机提供一种有效而准确的机制将手语翻译成文本或语音。目前最新发展水平的手语识别系统在实际应用中应解决非特定人连续手语问题。提出一种将连续手语识别分解成各弧立词识别的分治方法，用于非特定人连续手语识别。把精简循环网（simple recurrent network,简称SRN）作为连续手语的段边界检测器，把SRN分段结果作为隐马可夫模型（hidden Markov models,简称HMM）框架中的状态输入，在HMM框架里使用网格Viterbi算法搜索出一条最佳手语词路径。实验结果表明，该方法的识别效果比单纯使用HMM要好。     

A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition

The role of gesture recognition is significant in areas like human‐computer interaction, sign language, virtual reality, machine vision, etc. Among various gestures of the human body, hand gestures play a major role to communicate nonverbally with the computer. As the hand gesture is a continuous pattern with respect to time, the hidden Markov model (HMM) is found to be the most suitable pattern recognition tool, which can be modeled using the hand gesture parameters. The HMM considers the speeded up robust feature features of hand gesture and uses them to train and test the system. Conventionally, the Viterbi algorithm has been used for training process in HMM by discovering the shortest decoded path in the state diagram. The recursiveness of the Viterbi algorithm leads to computational complexity during the execution process. In order to reduce the complexity, the state sequence analysis approach is proposed for training the hand gesture model, which provides a better recognition rate and accuracy than that of the Viterbi algorithm. The performance of the proposed approach is explored in the context of pattern recognition with the Cambridge hand gesture data set.     

Large vocabulary sign language recognition based on fuzzy decision trees

The major difficulty for large vocabulary sign recognition lies in the huge search space due to a variety of recognized classes. How to reduce the recognition time without loss of accuracy is a challenging issue. In this paper, a fuzzy decision tree with heterogeneous classifiers is proposed for large vocabulary sign language recognition. As each sign feature has the different discrimination to gestures, the corresponding classifiers are presented for the hierarchical decision to sign language attributes. A one- or two- handed classifier and a hand-shaped classifier with little computational cost are first used to progressively eliminate many impossible candidates, and then, a self-organizing feature maps/hidden Markov model (SOFM/HMM) classifier in which SOFM being as an implicit different signers' feature extractor for continuous HMM, is proposed as a special component of a fuzzy decision tree to get the final results at the last nonleaf nodes that only include a few candidates. Experimental results on a large vocabulary of 5113-signs show that the proposed method dramatically reduces the recognition time by 11 times and also improves the recognition rate about 0.95% over single SOFM/HMM.     

Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences

In the present paper, a trajectory model, derived from a hidden Markov model (HMM) by imposing explicit relationships between static and dynamic feature vector sequences, is developed and evaluated. The derived model, named a trajectory HMM, can alleviate two limitations of the standard HMM, which are (i) piece-wise constant statistics within a state and (ii) conditional independence assumption of state output probabilities, without increasing the number of model parameters. In the present paper, a Viterbi-type training algorithm based on the maximum likelihood criterion is also derived. The performance of the trajectory HMM was evaluated both in speech recognition and synthesis. In a speaker-dependent continuous speech recognition experiment, the trajectory HMM achieved an error reduction over the corresponding standard HMM. Subjective listening test results showed that the introduction of the trajectory HMM improved the naturalness of synthetic speech.     

一种改进的隐马尔可夫模型在语音识别中的应用

提出了一种新的马尔可夫模型——异步隐马尔可夫模型．该模型针对噪音环境下语音识别过程中出现丢失帧的情况，通过增加新的隐藏时间标示变量Ck，估计出实际观察值对应的状态序列，实现对不规则或者不完整采样数据的建模．详细介绍了适合异步HMM的前后向算法以及用于训练的EM算法，并且对转移矩阵的计算进行了优化．最后通过实验仿真，分别使用经典HMM和异步HMM对相同的随机抽取帧的语音数据进行识别,识别结果显示在抽取帧相同情况下异步HMM比经典HMM的识别错误率低．     

一种基于HMM的场景识别方法

隐马尔科夫模型作为一种统计分析模型,能够通过观测向量序列计算其隐含状态的概率分布密度。提出一种智能空间中基于HMM的场景识别方法,该方法指定系统相关情境信息,确定隐含场景集和观察情境集,采用部分相关情境信息而非全部情境信息作为场景特征参与场景识别,利用HMM对隐含场景间的关系进行建模,设计了基于HMM的场景识别算法。实验结果表明,采用基于HMM的场景识别方法能够获得较高的识别效率。     

基于改进粒子群算法的隐马尔可夫模型训练

针对隐马尔可夫模型传统训练算法易收敛于局部极值的问题,提出一种带极值扰动的自适应调整惯性权重和加速系数的粒子群算法,将改进后的粒子群优化算法引入到隐马尔可夫模型的训练中,分别对隐马尔可夫模型的状态数与参数进优化.通过对手写数字识别的实验说明,提出的基于改进粒子群优化算法的隐马尔可夫模型训练算法与传统隐马尔可夫模型训练算法Baum-Welch算法相比,能有效地跳出局部极值,从而使训练后的隐马尔可夫模型具有较高的识别能力.