基于HMM的分类器在联机手写藏文识别中的应用

为了解决联机手写藏文识别中藏文的曲线型笔划比较多,连笔情况很普遍以及相似字丁多等问题,提出了一种新的联机手写藏文识别方法:基于HMM分类器的联机手写藏文识别的方法.设计了三种不同的HMM分类器进行藏文字丁识别,实验结果表明,基于HMM分类器的联机手写藏文识别具有较高地识别率,前十位识别率可达93.9012%.     

连续语音识别前端鲁棒性研究

针对大词汇量连续语音识别中识别率不高的问题,提出了将语音增强级联在识别系统前端,在语音增强中将谱减法和对数最小均方误差算法（logmmse）与用于噪声估计的最小控制递归平均算法（imcra）相结合。识别系统使用Mel频率倒谱系数（MFCC）提取特征,用隐马尔科夫模型（HMM）训练与识别。实验结果表明,提出的方法最高能使单词识别率提高38.9%,使句子正确率提高21.8%。该方法用于大词汇量连续语音识别是可行的,有效的。     

基于FVQ/HMM的无教师说话人自适应

本文提出了一种新的语音识别方法,它综合了VQ、HMM和无教师说话人自适应算法的优点,在每个状态通过用矢量量化误差值取代传统HMM的输出概率值来建立FVQ/HMM,同时采用基于模糊矢量量化的无教师自适应算法,来改变FVQ/HMM的各状态的码字,从而实现对未知说话人的码本适应.本文通过非特定人汉语数码(孤立和连续数码)语音识别实验,把该新的组合方法同基于CHMM的自适应和识别方法进行了比较,实验结果表明该方法的自适应和识别效果优于基于CHMM的方法.     

基于TMS320C54x DSP的实时语音识别系统

介绍一个非特定人、小词汇表、孤立词的语音识别系统,它采用基于隐马尔可夫随机模型（HMM）的语音信号端点检测方法和基于VQ/HMM的自学习语音识别算法,同时以高速的TMS320C54xDSP芯片为核心进行硬件设计,实现语音的实时识别。     

基于HMM和ANN的语音情感识别研究

语音情感识别是语音识别中的重要分支,是和谐人机交互的基础理论。由于单一分类器在语音情感识别中的局限性,本文提出了隐马尔科夫模型（HMM）和人工神经网络（ANN）相结合的方法,对高兴、惊奇、愤怒、悲伤、恐惧、平静六种情感分别设计一个HMM模型,得到每种情感的最佳匹配序列,然后利用ANN作为后验分类器对测试样本进行分类,通...     

基于HMM和匹配追踪的多参数语音识别

本文提出了一种基于HMM和匹配追踪的多参数语音识别新算法。首先构建局部余弦基表示语音信号,对其进行匹配追踪(MP)分解。然后,应用改进后的算法对语音信号进行特征提取,得到余弦基原子参数和魏格纳-维利(WVD)分布,并结合语音信号的美尔频率倒谱系数(MFCC)一起作为该信号的特征向量,通过隐马尔科夫模型(HMM)进行识别。最后通过实验验证了方法的有效性,仿真实验表明,改进后的多参数语音识别算法比仅使用MFCC作为特征向量的HMM算法,提高了语音识别的速度和准确度。     

基于HMM的连续小词量语音识别系统的研究

为了提高语音识别效率及对环境的依赖性,文章对语音识别算法部分和硬件部分做了分析与改进,采用ARMS3C2410微处理器作为主控制模块,采用UDA1314TS音频处理芯片作为语音识别模块,利用HMM声学模型及Viterbi算法进行模式训练和识别,设计了一种连续的、小词量的语音识别系统。实验证明,该语音识别系统具有较高的识别率和一定程度的鲁棒性,实验室识别率和室外识别率分别达到95.6%,92.3%。     

基于HMM方法的动态手势轨迹训练性能研究

基于HMM(Hidden Markov Model,隐形马尔可夫模型)对动态手势轨迹的训练是手势识别的关键技术之一。本文对HMM的模型训练采用Baum.Welch算法,并分别从迭代次数,样本个数选取,以及模型初值选取等方面对动态手势轨迹的训练性能进行了研究。实验结果表明HMM方法对具有时空特性的动态手势轨迹识别是非常有效的。     

家电控制系统的语音关键词识别算法研究

介绍了一种适用于家电控制系统的语音关键词识别算法.算法包括对关键词的搜索与确认两部分.通过对关键词和非关键词分别建立模型,建立起一个并行识别网络.使用帧同步的Viterbi算法在网络上搜索关键词.为使关键词的识别结果精确,在对支持向量机(SVM)的原理进行简要分析后,提出将SVM作为分类器对基于HMM的最大似然搜索结果进行关键词确认.实验结果显示,联合采用HMM和SVM的关键词语音识别混合算法具有较高的性能.     

模糊语言模型在唇读系统中的应用

论文针对传统的统计语言模型所面临的数据稀疏和估计严苛性问题,提出基于模糊表示的n-元语法模型,并将其应用于唇语识别系统中,结合隐马尔科夫模型(Hidden Markov Model),建立了新的唇动识别模型—HFM(HMM and Fuzzy Language Model)。利用教育部语言文字应用研究所计算语言学研究室研制的语料库在线系统,制作了一个小型语料库,进行了句子识别实验。实验结果表明,HFM可使单音识别率最高提高6.5%,句子识别率最高提高22.7%,另外,采用语言模型对文字流进行解析,而不再是盲目文字匹配,单一视觉流的解析精确度达68.7%。      

Segmental probability distribution model approach for isolatedMandarin syllable recognition

A segmental probability distribution model (SPDM) approach is proposed for fast and accurate recognition of isolated Mandarin syllables. Instead of the conventional frame-based approach such as the hidden Markov model (HMM), the model matching process in the proposed SPDM is evaluated segment-by-segment based on information-theoretic distance measurements. The training and recognition procedures for the SPDM are developed first. Several distance measurement criteria, including the Chernoff distance, Bhattacharyya distance, Patrick-Fisher (1969) distance, divergence and a Bayesian-like distance, are used, and formulations and comparative results are discussed. Experimental results show that, compared to the widely used sub-unit based continuous density HMM, the proposed method leads to an improvement of 15.27% in the error rate, with a 12-fold increase in recognition speed and less than three quarters of the mixture requirements     

Improved maximum model distance for HMM training

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     

Human Activity Recognition Using Body Joint‐Angle Features and Hidden Markov Model

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.     

Neural networks for statistical recognition of continuous speech

In recent years there has been a significant body of work, both theoretical and experimental, that has established the viability of artificial neural networks (ANN's) as a useful technology for speech recognition. It has been shown that neural networks can be used to augment speech recognizers whose underlying structure is essentially that of hidden Markov models (HMM's). In particular, we have demonstrated that fairly simple layered structures, which we lately have termed big dumb neural networks (BDNN's), can be discriminatively trained to estimate emission probabilities for an HMM. Recently simple speech recognition systems (using context-independent phone models) based on this approach have been proved on controlled tests, to be both effective in terms of accuracy (i.e., comparable or better than equivalent state-of-the-art systems) and efficient in terms of CPU and memory run-time requirements. Research is continuing on extending these results to somewhat more complex systems. In this paper, we first give a brief overview of automatic speech recognition (ASR) and statistical pattern recognition in general. We also include a very brief review of HMM's, and then describe the use of ANN's as statistical estimators. We then review the basic principles of our hybrid HMM/ANN approach and describe some experiments. We discuss some current research topics, including new theoretical developments in training ANN's to maximize the posterior probabilities of the correct models for speech utterances. We also discuss some issues of system resources required for training and recognition. Finally, we conclude with some perspectives about fundamental limitations in the current technology and some speculations about where we can go from here     

基于HMM和PNN的混合语音识别模型

基于隐马尔可夫模型(HMM)和改进后的概率神经网络(PNN)模型提出了一种用于语音识别的混合模型,该模型首先利用HMM生成最佳语音状态序列,然后对最佳状态序列进行时间规整,最后通过PNN神经网络进行分类识别。给出了HMM参数训练及时间规整的算法。实验结果表明这种模型比HMM具有更好的识别效果。     

Identification of humans using gait

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.     

正反向隐马尔可夫模型及其在连续语音识别中的应用

本文针对语音信号中客观存在的正、反向依赖特性，明确提出了用条件概率的概念来定量表述语音信号的这种正、反向的马尔可大依赖关系，提出了描述语音信号这种正反向依赖关系的正反向隐马尔可夫模型（ＨＭＭ），并用实验证明了仅仅利用语音反向依赖关系语音识别同样也能获得相当可观的识别性能。接着，本文针对孤立字和连续语音两种不同的识别任务，研究了在语音识别中同时利用这两种依赖信息的方法，并提出了一种连续语音识别中的新的搜索算法──正反向分半混合搜索。这种方法利用基于正向ＨＭＭ的正向Ｖｉｔｅｒｂｉ搜索和基于反向ＨＭＭ的反向Ｖｉｔｅｒｂｉ搜索的中间结果来有效地结合正反向依赖信息，实验证明正反向分半混合搜索方法确实一致地优于单用任何一种依赖信息的单向搜索识别方法。     

DWT based HMM for face recognition

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.     

基于色度分析的唇动特征提取与识别

本文提出了一种基于色度滤波的唇动特征提取与识别方法 ,它通过唇的色度滤波 ,得到增强的唇动图像 ,再利用可变模板 ,描述口型轮廓并提取特征参数 ,并用HMM模型进行唇运动序列图像识别 .该方法鲁棒性强 ,对光照没有苛刻的要求 ,且针对非特定人 ,适用于自然条件下的实用环境 ,解决了可变模板对目标边缘有较高分辨率的要求 ,使方法更实用化 .本文的实验是基于单纯的视觉信息 (没有声音信道的信息 )的唇动识别 ,不加语音信息 ,实验集合只限于单韵母 ,识别率可达 95 8% .