A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Manifold learning based speaker dependent dimension reduction for robust text independent speaker verification

Speaker verification has been studied widely from different points of view, including accuracy, robustness and being real-time. Recent studies have turned toward better feature stability and robustness. In this paper we study the effect of nonlinear manifold based dimensionality reduction for feature robustness. Manifold learning is a popular recent approach for nonlinear dimensionality reduction. Algorithms for this task are based on the idea that each data point may be described as a function of only a few parameters. Manifold learning algorithms attempt to uncover these parameters in order to find a low-dimensional representation of the data. From the manifold based dimension reduction approaches, we applied the widely used Isometric mapping (Isomap) algorithm. Since in the problem of speaker verification, the input utterance is compared with the model of the claiming client, a speaker dependent feature transformation would be beneficial for deciding on the identity of the speaker. Therefore, our first contribution is to use Isomap dimension reduction approach in the speaker dependent context and compare its performance with two other widely used approaches, namely principle component analysis and factor analysis. The other contribution of our work is to perform the nonlinear transformation in a speaker-dependent framework. We evaluated this approach in a GMM based speaker verification framework using Tfarsdat Telephone speech dataset for different noises and SNRs and the evaluations have shown reliability and robustness even in low SNRs. The results also show better performance for the proposed Isomap approach compared to the other approaches.     

基于KL散度的支持向量机方法及应用研究

针对ICA提取的说话人语音特征,导出以库尔贝克—莱布勒（KL）散度作为距离测度的KL核函数用来设计支持向量机,实现了一个高分辨率的ICA/SVM说话人确认系统.说话人确认的仿真实验结果表明,使用ICA特征基函数系数比直接使用语音数据训练SVM得到的分类间隔大,支持向量少,而且使用KL核函数的ICA/SVM系统确认的等差率也低于其它传统SVM方法,证明了基于KL散度的支持向量机方法在实现分类和判决上具有高效性能.     

Dimensionality reduction using genetic algorithms

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces     

基于PCA和核Fisher判别的说话人确认

针对核Fisher判别技术在说话人确认中实时性较差的问题,提出了一种基于PCA和核Fisher判别的说话人确认方法.利用PCA进行特征向量的降维、去冗余,以减少后续计算的复杂度,提高说话人确认的速度,使用基于核函数的Fisher判别技术对说话人进行确认,从而在整体上提高系统的实时性.并通过实验验证了该方法的有效性.     

Quality measures for speaker verification with short utterances

The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model–universal background model (GMM–UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures.     

Extraction of Speaker Features from Different Stages of DSR Front-Ends for Distributed Speaker Verification

The ETSI has recently published a front-end processing standard for distributed speech recognition systems. The key idea of the standard is to extract the spectral features of speech signals at the front-end terminals so that acoustic distortion caused by communication channels can be avoided. This paper investigates the effect of extracting spectral features from different stages of the front-end processing on the performance of distributed speaker verification systems. A technique that combines handset selectors with stochastic feature transformation is also employed in a back-end speaker verification system to reduce the acoustic mismatch between different handsets. Because the feature vectors obtained from the back-end server are vector quantized, the paper proposes two approaches to adding Gaussian noise to the quantized feature vectors for training the Gaussian mixture speaker models. In one approach, the variances of the Gaussian noise are made dependent on the codeword distance. In another approach, the variances are a function of the distance between some unquantized training vectors and their closest code vector. The HTIMIT corpus was used in the experiments and results based on 150 speakers show that stochastic feature transformation can be added to the back-end server for compensating transducer distortion. It is also found that better verification performance can be achieved when the LMS-based blind equalization in the standard is replaced by stochastic feature transformation.     

Text-independent speaker identification based on selection of the most similar feature vectors

The speaker recognition has been one of the interesting issues in signal and speech processing over the last few decades. Feature selection is one of the main parts of speaker recognition system which can improve the performance of the system. In this paper, we have proposed two methods to find MFCCs feature vectors with the highest similar that is applied to text independent speaker identification system. These feature vectors show individual properties of each person’s vocal tract that are mostly repeated. They are used to build speaker’s model and to specify decision boundary. We applied MFCC of each window over main signal as a feature vector and used clustering to obtain feature vectors with the highest similar. The Speaker identification experiments are performed using the ELSDSR database that consists of 22 speakers (12 male and 10 female) and Neural Network is used as a classifier. The effect of three main parameters have been considered in two proposed methods. Experimental results indicate that the performance of speaker identification system has been improved in accuracy and time consumption term.     

Text-dependent speaker verification using classical LBG,adaptive LBG and FCM vector quantization

An important task of speaker verification is to generate speaker specific models and match an input speaker’s utterance with these models. This paper focuses on comparing the performance of text dependent speaker verification system using Mel Frequency Cepstral Coefficients feature and different Vector Quantization (VQ) based speaker modelling techniques to generate the speaker specific models. Speaker-specific information is mainly represented by spectral features and using these features we have developed the model which serves as an important entity for determining the claimed identity of the speaker. In the modelling part, we used Linde, Buzo, Gray (LBG) VQ, proposed adaptive LBG VQ and Fuzzy C Means (FCM) VQ for generating speaker specific model. The experimental results that are performed on microphonic database shows that accuracy significantly depends on the size of the codebook in all VQ techniques, and on FCM VQ accuracy also depend on the value of learning parameter of the objective function. Experiment results shows that how the accuracy of speaker verification system is depend on different representations of the codebook, different size of codebook in VQ modelling techniques and learning parameter in FCM VQ.     

Dynamic visual features for audio–visual speaker verification

The cascading appearance-based (CAB) feature extraction technique has established itself as the state-of-the-art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the visual speech recognition application also provide similar improvements for visual speaker recognition. A further study is conducted comparing synchronous HMM (SHMM) based fusion of CAB visual features and traditional perceptual linear predictive (PLP) acoustic features to show that higher complexity inherit in the SHMM approach does not appear to provide any improvement in the final audio–visual speaker verification system over simpler utterance level score fusion.     

基于特征相关性的特征选择

提出了一种基于特征相关性的特征选择方法。该方法以特征之间相互依赖程度（相关度）为聚类依据先对特征进行聚类,再从各特征簇中挑选出具有代表性的特征,然后在被选择出来的特征中删除与目标特征无关或是弱相关的特征,最后留下的特征作为最终的特征子集。理论分析表明该方法的运算效率高,时间复杂度低,适合于大规模数据集中的特征选择。在UCI数据集上与文献中的经典方法进行实验比较和分析,结果显示提出的特征选择方法在特征约减和分类等方面具有更好的性能。     

非线性局部寻优时间弯曲校正及签名特征空间稳定性研究

根据签名动态信息进行签名认证可以提高认证系统的安全性，它是在由签名动态信息的特征值张成的特征空间上的分类问题，然而，签名动态信息时间序列的时间弯曲现象使得特征值分离，不容易在特征空间上确定出真签名的特征值稳定的子空间，在签名样本数量小时尤为如此，因此提出一种非线性局部寻优时间弯曲校正方法，这具有较好的校正效果和较低的计算复要度，利用它对签名样本的动态信息时间序列进行校正，可以提高签名特征向量在特征空间上分布的聚扰性，拉开真，伪签名特征向量在特征空间上的距离，综合利用非线性局部寻优时间弯曲校正方法和线性时间弯曲校正方法对有限数量的标准签名样本进行处理，可在特征空间划分出不同置信度的特征稳定的子空间，以此满足不同安全程度认证的需要。     

基于多特征i-vector的短语音说话人识别算法

当测试语音时长充足时,单一特征的信息量和区分性足够完成说话人识别任务,但是在测试语音很短的情况下,语音信号里缺乏充分的说话人信息,使得说话人识别性能急剧下降。针对短语音条件下的说话人信息不足的问题,提出一种基于多特征i-vector的短语音说话人识别算法。该算法首先提取不同的声学特征向量组合成一个高维特征向量,然后利用主成分分析（PCA）去除高维特征向量的相关性,使特征之间正交化,最后采用线性判别分析（LDA）挑选出最具区分性的特征,并且在一定程度上降低空间维度,从而实现更好的说话人识别性能。结合TIMIT语料库进行实验,同一时长的短语音（2 s）条件下,所提算法比基于i-vector的单一的梅尔频率倒谱系数（MFCC）、线性预测倒谱系数（LPCC）、感知对数面积比系数（PLAR）特征系统在等错误率（EER）上分别有相对72.16%、69.47%和73.62%的下降。不同时长的短语音条件下,所提算法比基于i-vector的单一特征系统在EER和检测代价函数（DCF）上大致都有50%的降低。基于以上两种实验的结果充分表明了所提算法在短语音说话人识别系统中可以充分提取说话人的个性信息,有利地提高说话人识别性能。     

Robust Text-Independent Speaker Verification Using Genetic Programming

Robust automatic speaker verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, genetic programming offers inherent feature selection and solutions that can be meaningfully analyzed, making it well suited to this task. This paper introduces a genetic programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. We also show the effect of a simulated telephone network on classification results which highlights the principal advantage, namely robustness to both additive and convolutive noise     

A fast feature selection technique in multi modal biometrics using cloud framework

Biometrics refers to the process that uses biological or physiological traits to identify individuals. The progress seen in technology and security has a vital role to play in Biometric recognition which is a reliable technique to validate individuals and their identity. The biometric identification is generally based on either their physical traits or their behavioural traits. The multimodal biometrics makes use of either two or more of the modalities to improve recognition. There are some popular modalities of biometrics that are palm print, finger vein, iris, face or fingerprint recognition. Another important challenge found with multimodal biometric features is the fusion, which could result in a large set of feature vectors. Most biometric systems currently use a single model for user authentication. In this existing work, a modified method of heuristics that is efficiently used to identify an optimal feature set that is based on a wrapper-based feature selection technique. The proposed method of feature selection uses the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are used to feature extraction and classification process utilizes the integration of face, and finger print texture patterns. The set of training images is converted to grayscale. The crossover operator is applied to generate multiple samples for each number of images. The wok proposed here is pre-planned for each weight of each biometric modality, which ensures that even if a biometric modality does not exist at the time of verification, a person can be certified to provide calculated weights the threshold value. The proposed method is demonstrated better result for fast feature selection in bio metric image authentication and also gives high effectiveness security.     

Speaker recognition using pyramid match kernel based support vector machines

Gaussian mixture model (GMM) based approaches have been commonly used for speaker recognition tasks. Methods for estimation of parameters of GMMs include the expectation-maximization method which is a non-discriminative learning based method. Discriminative classifier based approaches to speaker recognition include support vector machine (SVM) based classifiers using dynamic kernels such as generalized linear discriminant sequence kernel, probabilistic sequence kernel, GMM supervector kernel, GMM-UBM mean interval kernel (GUMI) and intermediate matching kernel. Recently, the pyramid match kernel (PMK) using grids in the feature space as histogram bins and vocabulary-guided PMK (VGPMK) using clusters in the feature space as histogram bins have been proposed for recognition of objects in an image represented as a set of local feature vectors. In PMK, a set of feature vectors is mapped onto a multi-resolution histogram pyramid. The kernel is computed between a pair of examples by comparing the pyramids using a weighted histogram intersection function at each level of pyramid. We propose to use the PMK-based SVM classifier for speaker identification and verification from the speech signal of an utterance represented as a set of local feature vectors. The main issue in building the PMK-based SVM classifier is construction of a pyramid of histograms. We first propose to form hard clusters, using k-means clustering method, with increasing number of clusters at different levels of pyramid to design the codebook-based PMK (CBPMK). Then we propose the GMM-based PMK (GMMPMK) that uses soft clustering. We compare the performance of the GMM-based approaches, and the PMK and other dynamic kernel SVM-based approaches to speaker identification and verification. The 2002 and 2003 NIST speaker recognition corpora are used in evaluation of different approaches to speaker identification and verification. Results of our studies show that the dynamic kernel SVM-based approaches give a significantly better performance than the state-of-the-art GMM-based approaches. For speaker recognition task, the GMMPMK-based SVM gives a performance that is better than that of SVMs using many other dynamic kernels and comparable to that of SVMs using state-of-the-art dynamic kernel, GUMI kernel. The storage requirements of the GMMPMK-based SVMs are less than that of SVMs using any other dynamic kernel.     

Local fuzzy PCA based GMM with dimension reduction on speaker identification

To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix on each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method shows faster result with less storage maintaining same performance.     

基于模型距离和支持向量机的说话人确认

针对采用支持向量机的说话人的确认问题,提出采用背景模型、说话人模型、测试语句模型间距离和夹角作为支持向量机的特征矢量,同时将组特征矢量与广义线性判别式序列核函数的参数相拼接,能够取得相对于基线的混合高斯模型算法更高的识别率.在2004年NIST评测数据库上,采用推荐算法的系统等错误率比基线的混合高斯-背景模型系统低16%.对说话人识别取得一定进展.     

语音识别中基于i-vector的说话人归一化研究

i-vector是反映说话人声学差异的一种重要特征,在目前的说话人识别和说话人验证中显示了有效性。将i-vector应用于语音识别中的说话人的声学特征归一化,对训练数据提取i-vector并利用LBG算法进行无监督聚类．然后对各类分别训练最大似然线性变换并使用说话人自适应训练来实现说话人的归一化。将变换后的特征用于训练和识别．实验表明该方法能够提高语音识别的性能。     

Adaptive simplification of solution for support vector machine

SVM has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. Unfortunately, SVM is currently considerably slower in test phase caused by number of the support vectors, which has been a serious limitation for some applications. To overcome this problem, we proposed an adaptive algorithm named feature vectors selection (FVS) to select the feature vectors from the support vector solutions, which is based on the vector correlation principle and greedy algorithm. Through the adaptive algorithm, the sparsity of solution is improved and the time cost in testing is reduced. To select the number of the feature vectors adaptively by the requirements, the generalization and complexity trade-off can be directly controlled. The computer simulations on regression estimation and pattern recognition show that FVS is a promising algorithm to simplify the solution for support vector machine.