共查询到20条相似文献,搜索用时 31 毫秒
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A new approach based on the maximum model distance (IMMD) approach for HMM speech recognition systems is proposed. It defines a more realistic model distance definition for HMM training, and utilises the limited training data in a more effective manner. Theoretical and practical issues concerning this approach are investigated. Experimental results showed that a significant reduction in errors could be achieved with this new approach when compared with the maximum model distance (MMD) criterion 相似文献
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在探地雷达探测过程中,天线相对目标的远近变化反映在面向深度的一维时域信号(A-scan)所组成的序列的变化过程中,由此提出一种针对变化过程建模的目标识别方法。在特征提取环节,提出将时频分析与图像纹理分析相结合,首先计算A-scan信号的二维时频联合分布图像,再利用特定的图像纹理描述算子构造特征向量。识别过程根据目标与天线间距离的变化,采用无跨越单向连续隐马尔可夫模型(HMM)对序列的变化过程建模。实验表明这种基于变化过程的HMM方法比无序地利用单条A-scan特征的支持向量机方法具有更好的效果。 相似文献
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The authors propose a channel compensation method for the hidden Markov model (HMM) parameters in automatic speech recognition. The proposed approach is to adapt the existing reference models to a new channel environment by using a small amount of adaptation data. The concept of HMM parameter adaptation by incorporating the corresponding phone-dependent channel compensation (PDCC) vectors is applied to improve the performance of speech recognition. Two extended PDCC techniques are presented. One is based on the refinement of PDCC using vector quantisation. The other is based on the interpolation of compensation vectors. Both techniques are evaluated on the experiments on telephone speech recognition and speaker adaptation. The experimental results show that the performance can be significantly improved 相似文献
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This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model (HMM) and artificial neural network (ANN). First, the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second, with the probability score as input to the neural network, the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally, Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of neural network, the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover, the hybrid model enhances the individual flaws of the HMM and the neural network and greatly improves the speed and performance of speech recognition. 相似文献
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为了能够综合利用隐马尔可夫模型(HMMs)分类器在分类过程中能够得到的多种信息,提出一种基于距离相似性度量对HMMs后验概率进行调整的方法,将样本相似性与HMMs后验概率有机地结合起来进行识别。在分类过程中,采用距离相似性度量来描述待识别样本与模式类标准样本间的相似性,然后采用归一化距离相似性度量对后验概率进行适当调整,最后用调整后的概率进行分类。实验结果表明:与标准的HMMs识别方法相比,改进后的方法能够在计算量增加很小的情况下,较好地改善系统的识别精度;系统性能的改善效率在1.1~6.5间。 相似文献
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线性预测HMM(Linear Prediction HMM,LPHMM)并没有象传统HMM那样引入状态输出独立同分布假设,但实用中识别性能并不佳.通过分析两种HMM的各自优劣,本文提出了一种新的语音识别的混合模型,将语音静态特性(基于传统HMM)和动态特性(基于LPHMM)分别描述又有机结合在一起,更为精确地刻划了真实的语音现象,同时又继承使系统的实现改动很小和较小的计算量.汉语大词汇量非特定人连续语音识别的实验表明,混合模型的识别性能显著好于LPHMM和传统HMM.理论上,本文还给出了LPHMM的一组闭式参数重估公式. 相似文献
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This paper presents a novel approach for human activity recognition (HAR) using the joint angles from a 3D model of a human body. Unlike conventional approaches in which the joint angles are computed from inverse kinematic analysis of the optical marker positions captured with multiple cameras, our approach utilizes the body joint angles estimated directly from time‐series activity images acquired with a single stereo camera by co‐registering a 3D body model to the stereo information. The estimated joint‐angle features are then mapped into codewords to generate discrete symbols for a hidden Markov model (HMM) of each activity. With these symbols, each activity is trained through the HMM, and later, all the trained HMMs are used for activity recognition. The performance of our joint‐angle–based HAR has been compared to that of a conventional binary and depth silhouette‐based HAR, producing significantly better results in the recognition rate, especially for the activities that are not discernible with the conventional approaches. 相似文献
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Identification of humans using gait 总被引:24,自引:0,他引:24
Kale A. Sundaresan A. Rajagopalan A.N. Cuntoor N.P. Roy-Chowdhury A.K. Kruger V. Chellappa R. 《IEEE transactions on image processing》2004,13(9):1163-1173
We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases. 相似文献
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本文针对老年人日常活动类型及特点提出了一种基于三轴加速度传感器和HMM(Hidden Markov Model)的活动识别方法.本文首先提取了针对老年人相异、相似活动的标准差、能量、相关系数、RAF(RAtio Forward)、RVF(Ratio Vertical Forward)等特征值.然后定义老年人的HMM活动识别模型.最后在经过Baum-Welch算法对HMM进行参数训练后使用Viterbi算法来进行老年人活动识别.实验结果表明,本文方法适用于老年人的日常活动的识别,平均识别精度达到了93.3%,尤其是对于相似步态活动的识别准确率达到了93.7%. 相似文献
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DWT based HMM for face recognition 总被引:1,自引:0,他引:1
Shen Linlin Ji Zhen Bai Li Xu Chen 《电子科学学刊(英文版)》2007,24(6):835-837
A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence ex- traction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available. 相似文献
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The authors propose a new training approach based on maximum model distance (MMD) for HMMs. MMD uses the entire training set to estimate the parameters of each HMM, while the traditional maximum likelihood (ML) only uses those data labelled for the model. Experimental results showed that significant error reduction can be achieved through the proposed approach. In addition, the relationship between MMD and corrective training was discussed, and we have proved that the corrective training is a special case of the MMD approach 相似文献
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《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1987,75(9):1172-1184
A programmable VLSI architecture is described for efficiently computing a variety of kernel operations for speech recognition. These operations include dynamic programming for isolated and connected word recognition using both the template matching approach and the Hidden Markov Model (HMM) approach, the use of finite-state grammars (FSG) for connected word recognition, and metric computations for vector quantization and distance measurement. These are collectively referred to as "graph search" operations since a diagram consisting of arcs and nodes is commonly used to illustrate the HMM or FSG. As well as being able to efficiently compute a wide class of speech processing operations, the architecture is useful in other areas such as image processing. A chip design has been completed using 1.75-µm CMOS design rules and combines both custom and standard cell aproaches. 相似文献
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《Journal of Location Based Services》2013,7(3-4):273-285
ABSTRACTIndoor localization and tracking systems are harvesting more attention from the researchers. Recently, several approaches for localization systems have been proposed that use the sensors which are available on smartphones. In this paper, a new filtering approach based on Hidden Markov Model (HMM) techniques to enhance the accuracy of the localization system is presented. The proposed approach filtered out the undesirable Received Signal Strength (RSS) values which affect the accuracy of the system using the hidden states. The proposed approach helps in the direction and the distance estimation. The results of the proposed approach show a significant improvement in terms of distance estimation and the filtering of the RSS values. 相似文献
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本文模拟人类对图案的认知识别机理,提出了一种基于阅读认知模式的特征提取方法,提取基于视觉信息的图案特征,并提出了一种基于基元拓扑关系建模的通用图案识别方法。利用滑动窗来实现对人类认知图案机制的模拟,通过滑动窗的滑动过程完成对图案局部结构特征提取以及空间拓扑关系的构建。在图案识别建模方法中,采用了人工神经网络和隐马尔科夫模型相结合的混合识别模型,利用人工神经网络的强大计算能力完成基元建模,结合隐马尔科夫模型的强大的处理时序数据的优势,实现了图案的整体拓扑结构建模。实验结果验证了本文提出的图案识别方发的有效性和通用性。 相似文献
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语音识别隐马尔可夫模型的改进 总被引:7,自引:1,他引:6
由于在语音识别中被广泛应用的隐马尔可夫模型是一重马尔可夫模型,它不能充分地描述语音信号的时间相依性。虽然理论上可将HMM扩展成多重马尔可夫模型,但由于所需运算量和存储量将成指数增长而使其难以应用。因此,本文提出一种新模型,它是由HMM与一个能描述语音信号时间相依性的多维高斯密度函数相结合构成的。本文从理论上论证了新模型的合理性。对汉语不计声调的全部409个单音节的识别实验结果表明:新模型的识别率显 相似文献
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A new speaker adaptation technique for the hidden Markov model (HMM) based on the maximum model distance (Kwong, 1998; He, 1999) approach is presented. Experimental results have shown that this technique provides good performance even with a small amount of adaptation data. When these results are compared with those from the Baum-Welch approach and the stochastic matching approaches (Sank, 1996), it is found that the presented approach provides the best performance 相似文献