Pattern recognition of manufacturing process signals using Gaussian mixture models-based recognition systems

Unnatural patterns exhibited in manufacturing processes can be associated with certain assignable causes for process variation. Hence, accurate identification of various process patterns (PPs) can significantly narrow down the scope of possible causes that must be investigated, and speed up the troubleshooting process. This paper proposes a Gaussian mixture models (GMM)-based PP recognition (PPR) model, which employs a collection of several GMMs trained for PPR. By using statistical features and wavelet energy features as the input features, the proposed PPR model provides more simple training procedure and better generalization performance than using single recognizer, and hence is easier to be used by quality engineers and operators. Furthermore, the proposed model is capable of adapting novel PPs through using a dynamic modeling scheme. The simulation results indicate that the GMM-based PPR model shows good detection and recognition of current PPs and adapts further novel PPs effectively. Analysis from this study provides guidelines in developing GMM – based SPC recognition systems.     

高斯超向量-支持向量机鉴别性语种识别系统

支持向量机在语种识别技术中获得了广泛的研究和应用,并且达到和传统混合高斯模型相当的性能。高斯超向量-支持向量机系统将高斯混合模型与支持向量机有效地结合起来,采用高斯超向量核函数,以支持向量机作为后端分类器。重点介绍基于高斯超向量-支持向量机的语种识别系统,并和传统的高斯混合模型系统进行比较。在美国国家标准技术研究院2003年和2007年语种识别评测数据集上进行实验。实验结果表明,高斯超向量-支持向量机系统相对于混合高斯模型建模的方法,在长时数据上有较明显的性能优势。     

Efficient data selection for speech recognition based on prior confidence estimation using speech and monophone models

This paper proposes an efficient speech data selection technique that can identify those data that will be well recognized. Conventional confidence measure techniques can also identify well-recognized speech data. However, those techniques require a lot of computation time for speech recognition processing to estimate confidence scores. Speech data with low confidence should not go through the time-consuming recognition process since they will yield erroneous spoken documents that will eventually be rejected. The proposed technique can select the speech data that will be acceptable for speech recognition applications. It rapidly selects speech data with high prior confidence based on acoustic likelihood values and using only speech and monophone models. Experiments show that the proposed confidence estimation technique is over 50 times faster than the conventional posterior confidence measure while providing equivalent data selection performance for speech recognition and spoken document retrieval.     

Pattern recognition using Markov random field models

In this paper, we propose Markov random field models for pattern recognition, which provide a flexible and natural framework for modelling the interactions between spatially related random variables in their neighbourhood systems. The proposed approach is superior to conventional approaches in many aspects. This paper introduces the concept of states into Markov random filed models, presents a theoretic analysis of the approach, discusses issues of designing neighbourhood system and cliques, and analyses properties of the models. We have applied our method to the recognition of unconstrained handwritten numerals. The experimental results show that the proposed approach can achieve high performance.     

Pattern recognition categories

We present a categorical definition of pattern recognition, which unifies algebraic and topological formalisms. We state and prove a recognition theorem, and show how to define equivalence and invariants for both settings.     

Mixture control chart patterns recognition using independent component analysis and support vector machine

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.     

Pattern recognition of collections

The task of simultaneously classifying a set of objects has unusual problems. Three categories of such problems are identified. Characteristics of context-dependent and context-independent classifications are considered. The effects of having parallepipeds or ellipsoids as decision regions are compared.     

基于高斯混合模型的自然环境声音的识别

提出了一种基于高斯混合模型（GMM）的自然环境声音的识别方法。提取Mel频率倒谱系数（MFCCs）来分析声音信号;对于每种声音使用期望最大化算法基于MFCC特征集建立高斯混合模型;使用最小错误率判决规则和投票裁决的方法进行识别。使用GMM对36种自然环境的声音进行识别的正确率可达95.83%,且识别效果优于K最近邻（KNN）。     

Pattern classes: A technique for recovering their distributions

Many statistical pattern-recognition techniques depend for their application on the generation of one or more prototype patterns for each decision class. In turn, the determination of prototypes is dependent on the underlying probability distribution associated with a given class and that distribution's relationship to the distributions associated with the remaining classes. If these distributions are known, the problem of classification is considerably less complex than if they are unknown. The problem of recovering an unknown underlying distribution is one that has received considerable attention. The results thus far, however, are nonpractical. A practical technique that makes use of certain parameters related to sample size is presented and verified.This work was supported in part by the Office of Naval Research under Contract No. N00014-72-C-0459.Former Member of the Professional Staff, Ultrasystems, Inc.     

Gaussian kernels for incomplete data

This paper discusses a method to estimate the expected value of the Gaussian kernel in the presence of incomplete data. We show how, under the general assumption of a missing-at-random mechanism, the expected value of the Gaussian kernel function has a simple closed-form solution. Such a solution depends only on the parameters of the Gamma distribution which is assumed to represent squared distances. Furthermore, we show how the parameters governing the Gamma distribution depend only on the non-central moments of the kernel arguments, via the second-order moments of their squared distance, and can be estimated by making use of any parametric density estimation model of the data distribution. We approximate the data distribution with the maximum likelihood estimate of a Gaussian mixture distribution. The validity of the method is empirically assessed, under a range of conditions, on synthetic and real problems and the results compared to existing methods. For comparison, we consider methods that indirectly estimate a Gaussian kernel function by either estimating squared distances or by imputing missing values and then computing distances. Based on the experimental results, the proposed method consistently proves itself an accurate technique that further extends the use of Gaussian kernels with incomplete data.     

Pattern recognition using type-II fuzzy sets

Type II fuzzy sets are a generalization of the ordinary fuzzy sets in which the membership value for each member of the set is itself a fuzzy set in [0,1]. We introduce a similarity measure for measuring the similarity, or compatibility, between two type-II fuzzy sets. With this new similarity measure we show that type-II fuzzy sets provide us with a natural language for formulating classification problems in pattern recognition.     

基于Gammatone滤波器组的说话人识别算法研究

从人类听觉特性的角度出发,给出了一种基于人耳耳蜗听觉模型的Gammatone滤波器组模型。提出了一种新的Gammatone频率倒谱系数提取的算法,并在高斯混合模型识别系统中进行仿真实验。实验结果表明,采用这种新算法提取的Gammatone频率倒谱系数,应用于说话人识别系统,系统的识别率及鲁棒性都有明显的提高。     

Conception of complex probabilistic neural network system for classification of partial discharge patterns using multifarious inputs

Pattern recognition has a long history within electrical engineering but has recently become much more widespread as the automated capture of signal and images has been cheaper. Very many of the application of neural networks are to classification, and so are within the field of pattern recognition and classification. In this paper, we explore how probabilistic neural networks fit into the earlier framework of pattern recognition of partial discharge patterns since the PD patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. Also this paper describes a method for the automated recognition of PRPD patterns using a novel complex probabilistic neural network system for the actual classification task. The efficacy of composite neural network developed using probabilistic neural network is examined.     

Source cell-phone recognition from recorded speech using non-speech segments

In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yield higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42% to 98.39% and equal error rate (EER) reduces from 1.20% to 0.47% when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.     

Pattern classification of non-sparse data using optimal interpolative nets

In this paper we introduce two pattern classifiers for non-sparse data (i.e. data with overlapping class distributions) which use the optimal interpolative neural network (OI-net), derived by one of the authors based on a generalized Fock (GF) space formulation. We present a statistical pattern classifier operating as a two-stage algorithm. The first stage consists of a pre-processing operation involving a k-N N editing of the original training set T. The operation results in a new training set, T_e, which in the second stage is classified by an OI-net constructed by the recursive least squares algorithm. We also propose a new data specific classifier which has an additional third computational stage, in which samples of the original training set are added to the network piece by piece until satisfactory classification results are obtained. During the computation process the training set is iteratively updated until the number of mis-classified samples is minimized. The performance of these two classifiers has been evaluated in some illustrative examples.     

模式识别的主要方法及其应用

模式识别技术现在已经在各个领域得到广泛应用。本文对其理论基础与应用作了详细介绍与阐述。介绍了模式识别的基本概念、主要方法、模式识别的应用及其发展趋势。     

Unified probabilistic models for face recognition from a single example image per person

This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors‘ database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.     

语音识别中声效模式的分析及检测

针对声效相关的语音识别鲁棒性问题,在分析了声效变化情况下声强、时长、帧能量分布以及频谱倾斜能方面特性的基础上,建立了基于GMM的声效检测器。同时,还研究了声效变化对语音识别精度的影响,并提出了基于多模型框架的语音识别算法。汉语孤立词语音识别实验显示,除正常模式的语音识别精度略有下降外,其它四种声效模式的识别精度均有大幅度的提高。实验结果表明语音信号的声强、时长、帧能量分布以及频谱倾斜等信息能够用于识别声效模式,而多模型框架是解决声效相关的语音识别鲁棒性问题的有效方法。     

High-dimensional data clustering

Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually exist in different low-dimensional subspaces hidden in the original space. A family of Gaussian mixture models designed for high-dimensional data which combine the ideas of subspace clustering and parsimonious modeling are presented. These models give rise to a clustering method based on the expectation-maximization algorithm which is called high-dimensional data clustering (HDDC). In order to correctly fit the data, HDDC estimates the specific subspace and the intrinsic dimension of each group. Experiments on artificial and real data sets show that HDDC outperforms existing methods for clustering high-dimensional data.