高性能汉语数码串快速识别算法的算法

为了提高非特定人汉语数码串连续语音识别系统的识别速度,对系统进行了细致的研究,有针对性地提出了一种新的快速识别算法,通过对隐含马尔可夫模型输出概率密度函数运算的简化,以及采用结合段长信息的束搜索剪枝算法,在保证识别精度的情况下,使识别系统对不定长数码串平均识别时间从0．92s减少为0．11s,而串识别正确率仅从94．3％下降到94．0％,大大地提高了系统的整体性能。     

基于改进DTW的机器人语音指令识别系统研究

动态时间规整(Dynamic Time Warping)是语音识别中的一种经典算法,该算法简单有效,在实现孤立词识别系统中得到了广泛的应用.为了提高机器人语音识别系统的识别率和识别速度,文中采用了一种改进的DTW语音识别算法.在MATLAB 7.0环境下,对改进的语音端点检测和改进的DTW算法进行仿真实验,实验证明改进的算法提高了识别率,并且减少了识别所用的时间;将该算法移植到机器人上,在安静的环境下进行试验,结果表明机器人能准确而又快速地识别语音内容.最后,得到了改进的语音识别算法能够有效提高识别率和识别速度的结论.     

一种基于贪婪策略的说话人语音特征优选方法

为了提高说话人识别的性能,提出一种语音特征优选方法,从目前使用效果较好的特征参数中,采用贪婪算法优选出若干维特征用于识别。在TIMIT语音数据库上实验显示,识别率相比传统方法提高了1.6%;对于加入了噪声的语音,识别率提高了6%,识别速度提高了5倍左右。实验结果表明,优选后的特征参数能够去除不良特征对识别系统的干扰,有效提高说话人识别系统的识别率和识别速度。     

基于Viterbi改进算法的汉语数码语音识别系统

针对汉语语音识别中的一个分支——数码语音识别(MDSR)系统做出了具体的分析,并实验仿真了一个MDSR系统。在训练和识别阶段,引入了HMM模型的定义,为了提高识别的速度,还针对HMM系统中的核心识别算法Viterbi进行了改进,提出了一种累计积分判定的方法,对原Viterbi算法中的路径进行了剪裁,减少了冗余状态。使用MATLAB R2007a对此算法进行仿真实验,证明在相同语音识别系统的环境下,改进的Viterbi算法可以更有效地提高计算速度,且识别差错率没有明显的提高。     

基于粗糙集及RBF网络的英文字母识别

将粗糙集理论与神经网络相结合,针对7×5分辨率的大写英文字母,构建了基于RBF网络的字母识别系统,给出了该识别系统的核心算法与核心结构.该系统利用粗糙集中最小决策算法对识别矩阵进行属性约简,减少了大量的计算和数据库存储量,同时提高了系统识别速度和识别率.通过计算机模拟实验,将该识别系统的识别率与标准BP网络算法及改进BP网络算法相比较,证实了该系统的优越性,在有约1/7的像素点受到随机干扰的情况下,该系统识别率仍可达到88%以上.     

快速移不变稀疏分类算法在线识别汽油机故障

针对移不变稀疏编码算法在线运行时效率不高的问题,提出一种能够明显提高移不变稀疏编码效率的快速算法,并结合稀疏分类实现对汽油发动机故障的在线识别。该算法首先把移不变问题从时域转换到频域上,然后采用特征标记法和拉格朗日对偶法对稀疏系数和分类字典进行求解,在保证稀疏识别精度的同时大幅降低了问题求解的时间复杂度,从而有效改善了发动机故障在线识别系统的实时性。在发动机上的实验结果表明,该算法在怠速和1?500~2?000?r/min工况下对五种常见机械故障的平均识别精度分别为92.35%和91.44%,和其他识别算法大致持平。但其平均在线分类时间仅为13.91?ms和14.5?ms,且分类字典的平均训练速度同样仅为1.43?s和1.47?s,均明显快于其他识别算法。     

汉字联机手写识别系统的设计与实现

针对嵌入式手写识别系统存在识别率低、识别速率慢的缺点.研究了一种汉字联机手写识别的改进算法.首先从理论上介绍并分析了弹性网格识别算法和笔顺识别算法,接着将两种识别算法的优点进行有机组合,采用最小二乘法对输入的笔划进行线性拟合.算法在嵌入式linux下进行仿真设计并得到了实现,通过对仿真结果的分析,证明了结合后的改进算法达到了提高手写识别率和识别速率.所采用的方法对汉字手写体识别的研究有一定的借鉴和指导作用.     

基于改进DTW的机器人语音指令识别系统研究

动态时间规整(Dynamic Time Warping)是语音识别中的一种经典算法,该算法简单有效,在实现孤立词识别系统中得到了广泛的应用。为了提高机器人语音识别系统的识别率和识别速度,文中采用了一种改进的DTW语音识别算法。在MATLAB 7.0环境下,对改进的语音端点检测和改进的DTW算法进行仿真实验,实验证明改进的算法提高了识别率,并且减少了识别所用的时间;将该算法移植到机器人上,在安静的环境下进行试验,结果表明机器人能准确而又快速地识别语音内容。最后,得到了改进的语音识别算法能够有效提高识别率和识别速度的结论。     

主动式射频识别系统的设计与应用

设计一种工作于443 MHz频段的主动式射频识别系统的硬件结构,制定了物理层与数据链路层通信协议,描述了阅读器与射频卡工作流程,并给出系统待识别射频卡与被识别射频卡的关系曲线图。系统采用随机推迟防碰撞算法,提高了可靠性和识别率。射频卡工作于休眠工作模式延长了系统可持续使用时间。识别卡为120张,识别时间为8 s时,识别率可达98.33%。     

一种快速名片字符识别算法

针对嵌入式名片识别系统中低质名片字符特征提取困难导致识别速度慢和识别率低的问题,提出使用字符加权模板和基于统计的字符特征提取算法相结合,对提取的特征进行编码匹配.该算法对字符模板进行加权操作和网格化处理,并对每一块网格区域提取特征,并将提取出的特征在特征空间进行多尺度划分并采用二进制编码,这样用一串编码表示特征,最后通过编码匹配实现特征匹配.实验结果表明,该模板加权和特征提取方法与特征编码匹配结合可以较大地提高名片字符识别率.     

基于层级策略的连续数字串识别的研究

在分析汉语数字串语音特点的基础上,设计出了基于层级策略的连续数字串识别系统。该系统先对连续数字串进行确定性的预分割,再用LevelBuilding算法对每个分割段进行基于模板模糊分组的识别,在该识别结果的基础上利用加权矩阵识别算法进一步区分易混淆语音对。该系统在计算时间减少到原来的35.2%的同时识别率提高到94.08%。     

语音人机界面标引系统的实现

本文实现了一个媒体资产管理中的语音人机界面标引系统。系统以连续混合高斯隐马尔可夫模型为基础，采用分层构筑维特比算法进行训练和识别。为实现标引的实时性．采用实时计算的方法。为了减少计算量．并没有将状态持续时间分布引入Viterbi译码，而是将其作为后处理部分。对于数字识别，以声调作为辅助判决。以此做了一个体育赛事的词库．测试表明，标引系统首选识别率达到93．5％，前五选识别率达到98％。     

A model-based connected-digit recognition system using either hidden Markov models or templates

Although a great deal of effort has gone into studying large-vocabulary speech-recognition problems, there remains a number of interesting, and potentially exceedingly important, problems which do not require the complexity of these large systems. One such problem is connected-digit recognition, which has applications to telecommunications, order entry, credit-card entry, forms automation, and data-base management, among others. Connected-digit recognition is also an interesting problem for another reason, namely that it is one in which whole-word training patterns are applicable as the basic speech-recognition unit. Thus one can bring to bear all the fundamental speech recognition technology associated with whole-word recognition to solve this problem. As such, several connected digit recognizers have been proposed in the past few years. The performance of these systems has steadily improved to the point where high digit-recognition accuracy is achievable in a speaker-trained mode.In this paper we present a unified system for automatically recognizing fluently spoken digit strings based on whole-word reference units. The system that we will describe can use either hidden Markov model (HMM) technology or template-based technology. In fact the overall system contains features from both approaches.A key factor in the success of the various connected digit recognizers is the ability to derive, via a training procedure, a good set of representations of the behavior of the individual digits in actual connected digit strings. For most applications, isolated digit training does not provide a good enough characterization of the variability of the digits in strings. The “best” training procedure is to derive the digit reference patterns (either templates or statistical models) from connected digit strings. Such a connected word training procedure, based on a segmental k-means loop, has been proposed and was tested on seven experienced users of speech recognizers. For these seven talkers, average string accuracies of greater than 98% for unknown length strings, and greater than 99% for known length strings were obtained on an independent test set of 525 variable length strings (1–7 digits) recorded over local dialed-up telephone lines.To evaluate the performance of the overall connected digit recognizer under more difficult conditions, a set of 50 people (25 men, 25 women), from the non-technical local population, was each asked to record 1200 random digit strings over local dialed-up telephone lines. Both a speaker-trained and a multi-speaker training set was created, and a full performance evaluation was made. Results show that the average string accuracy for unknown- and known-length strings, in the speaker-trained mode, was 98% and 99% respectively; in the multi-speaker mode the average string accuracies were 94% and 96.6% respectively. A complete analysis of these results is given in this paper.     

一个汉语连续数字语音识别系统的实现

本文研究了基于互信息估计的汉语连续数字语音识别系统,其中包括语音信号的预处理部分、识别模板的训练、识别匹配算法以及后续处理部分,文中就系统的各个部分的具体实现进行了阐述,给出了系统部分识别结果,指出了系统的一些可以改进的方向。     

Extraction and optimization of B-spline PBD templates forrecognition of connected handwritten digit strings

The recognition of connected handwritten digit strings is a challenging task due mainly to two problems: poor character segmentation and unreliable isolated character recognition. The authors first present a rational B-spline representation of digit templates based on Pixel-to-Boundary Distance (PBD) maps. We then present a neural network approach to extract B-spline PBD templates and an evolutionary algorithm to optimize these templates. In total, 1000 templates (100 templates for each of 10 classes) were extracted from and optimized on 10426 training samples from the NIST Special Database 3. By using these templates, a nearest neighbor classifier can successfully reject 90.7 percent of nondigit patterns while achieving a 96.4 percent correct classification of isolated test digits. When our classifier is applied to the recognition of 4958 connected handwritten digit strings (4555 2-digit, 355 3-digit, and 48 4-digit strings) from the NIST Special Database 3 with a dynamic programming approach, it has a correct classification rate of 82.4 percent with a rejection rate of as low as 0.85 percent. Our classifier compares favorably in terms of correct classification rate and robustness with other classifiers that are tested     

一种改进的模板匹配的数字识别算法

提出了一种改进的模板匹配的数字识别算法,该算法是预先将字符分成若干个集合,经细化得到数字中央的骨骼部分,再对待识别数字提取特征并与训练库中的数字特征加权比较,利用欧式距离最小原则来对数字作出判决,试验结果表明,加权的模板匹配法保证了数字识别的正确率,而对数字进行预分类和细化处理,可以大大缩小模板匹配的识别速度,弥补了模板匹配算法对于大量数字耗时多的缺点,提升了系统速度。     

A Robust Viterbi Algorithm Against Impulsive Noise With Application to Speech Recognition

The Viterbi algorithm has been successfully applied to different pattern recognition and communication tasks. However, if some observations are corrupted by unknown impulsives noise which are not accounted for by the distortion measures, recognition performance can degrade significantly. In this paper, we propose a robust Viterbi algorithm to handle short impulsive noises with unknown characteristics by means of joint decoding and detection during the Viterbi search. To make the algorithm applicable to different noisy conditions with varying amounts of impulsive noise, we further proposed an approach to efficiently estimate the number of corruptions. We demonstrate the effectiveness of the proposed robust algorithms using spoken digit recognition experiments under two different impulsive noise environments. Under random Gaussian replacement noise, the proposed algorithm reduced digit error by more than 65%. Under the GSM network environment in which lost frames are replaced by interpolated neighboring frames, the robust algorithm reduced digit error by 20%. Furthermore, the proposed algorithm does not degrade performance when impulsive noise is not present.     

基于二级复合链码的七段数字识别方法

针对动态数字图像的识别问题,提出基于二级复合链码的七段数字识别方法。对细化后的目标图像进行第1级8方向Freeman链码描述,在此基础上,进行第2级4方向Freeman链码描述,得到的复合链码与七段数字具有单一映射关系。在无线瓦斯检测系统中的应用结果表明,该方法具有较高的识别率和执行效率。     

Evaluation of convolutional neural networks for visual recognition

Convolutional neural networks provide an efficient method to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. This network topology has been applied in particular to image classification when sophisticated preprocessing is to be avoided and raw images are to be classified directly. In this paper two variations of convolutional networks-neocognitron and a modification of neocognitron-are compared with classifiers based on fully connected feedforward layers with respect to their visual recognition performance. For a quantitative experimental comparison with standard classifiers two very different recognition tasks have been-chosen: handwritten digit recognition and face recognition. In the first example, the generalization of convolutional networks is compared to fully connected networks; in the second example human face recognition is investigated under constrained and variable conditions, and the limitations of convolutional networks are discussed.     

深度EM胶囊网络全重叠手写数字识别与分离EI北大核心CSCD

基于胶囊网络的向量神经元思想和期望最大算法(Expectation-maximization,EM),设计了一种以EM为向量聚类算法的深度胶囊网络(Deep capsule network,DCN),实现了重叠手写数字的识别与分离.该网络由两部分组成,第1部分是“识别网络”,将EM算法改为EM向量聚类算法,以替换原胶囊网络CapsNet中的迭代路由部分,这一改动优化了网络的运算过程,实现了重叠数字识别.第2部分是“重构网络”,由结构完全相同的两个并行网络组成,对双向量进行并行重构,实现了重叠数字的分离.实验结果显示,对于100%全重叠手写数字图片本网络识别率达到了96%,对比CapsNet在80%的重叠率下95%的识别率,本文网络在难度提升的情况下,识别率有明显提高,能够将完全重叠的两张手写数字进行图片进行准确地分离.