基于MRSVM的说话人辨识方法

提出一种新的基于多约简SVM的说话人辨识方法.先通过基于熵的特征筛选法,对训练样本进行维数约简,并改善聚类性能.然后用基于核的可能性聚类算法(KPCM)在特征空间选择最具有代表性的样本训练约简SVM,减少系统的存储量和训练量.实验结果表明,提出的方法在不影响识别率的情况下提高了识别速度,减少了SVM的计算量.     

基于VQ-MAP和SVM融合的说话人识别系统

针对传统支持向量机（SVM）在说话人识别中运算量过大的问题,提出了VQ-MAP和SVM融合的说话人识别系统。它应用仅自适应均值向量的最大后验概率矢量量化过程（VQ-MAP）,来得到自适应的说话人模型,用此模型中的参数向量作为支持向量应用于SVM来进行说话人识别。用Matlab进行仿真实验,结果表明,基于VQ-MAP和SVM融合的说话人识别系统大大降低了运算量,SVM训练时间短,且具有较高的识别率。     

基于子聚类约简支持向量机的说话人识别研究

由于支持向量具有边界性,在利用语音训练集对基于支持向量机（SVM）的说话人识别系统进行训练之前,需要对该训练集进行约简。考虑到该训练集一般十分庞大且具有非线性可分的特性,提出子聚类约简的概念。首先对训练集进行模糊核子聚类并过滤掉非边界的聚类区,然后依照提出的算法对保留的聚类区中的向量集做进一步地约简,使支持向量集更加集中在边界。理论和实践表明,经过两层的约简既保留了充足支持向量,保证了SVM良好的泛化性能,又提高了系统的时间和空间效率。     

基于改进PSCO-SVM的多生物特征融合技术的研究

为了解决单通道生物特征识别的缺陷,在信息融合的基础上提出了一种基于人脸和语音融合的生物特征识别模型,实现了特征层的融合。对人脸图像采用主成分分析法(PCA)进行特征提取,对说话人采用fisher判别进行特征维数的约简。同时,提出一种基于PSO的多粒子群协调优化(PSCO)方法,并将其用于训练SVM来实现人脸和语音的混合认证系统,实验结果表明该方法取得了较好的识别效果。     

用于SVM话者模型训练的冒认话者选取

在基于支持向量机（SVM）的文本无关的说话人确认中,为提高SVM话者模型的训练效率和区分性能,提出2种基于高斯混合模型（GMM）的冒认话者选取方法-通过GMM概率评分,为每个目标说话人选取最接近的话者作为冒认话者用于SVM话者模型的训练,不仅提高模型的训练效率,而且提高SVM模型的区分性,有效地改进系统性能。在NIST’04 Iside—Iside数据库上的实验表明该方法的有效性。     

利用PCA进行快速人脸检测

本文提出了一种改进的快速人脸检测方法。训练阶段节约了训练支持向量机（SVM）的时间;检测阶段利用肤色在YCbCr空间的聚类性和肤色符合高斯分布的特点,建立肤色模型并分割皮肤颜色。分割区域进行连通域体态分析后,用PCA方法将待检测样本降维处理再用SVM检测。实验结果表明,这种方法的检测效果令人满意。     

基于类内方差归一化和SVM的说话人识别方法

支持向量机（SVM）由于其强大的分类能力,引起人们广泛的重视,并且成功地应用于说话人识别。其中基于GLDS核的SVM系统性能比较优异。引入类内方差归一化（WCCN）方法来处理SVM的输入特征向量,并和GLDS核相结合,提出一种基于类内方差归一化和SVM的说话人识别方法。该方法利用WCCN方法对SVM的输入特征向量进行变换,增强特征向量的类间区分能力,再采用GLDS核函数进行SVM的训练,以提高SVM的分类效果。实验表明,新方法是有效的,其性能优于基于GLDS核的SVM系统。     

基于PCA和SVM的电磁模板分析攻击

在分析密码芯片电磁辐射数据相关性的基础上,提出了一种基于主成分分析(PCA)技术和多分类支持向量机(SVM)的模板分析密码旁路攻击方法。将密码设备运行时采集到的泄漏的电磁信号经过PCA处理之后作为特征向量,其对应的密钥作为类别,用已知密钥情况下获得的样本训练多分类SVM,用训练好的多分类SVM对未知密钥的电磁信号进行分类,并根据分类结果推测密钥值。实验表明,在用相同多个主成分和训练样本的条件下,SVM的分类效果好于大多数文献上使用的Bayes判别的分类效果。     

最小二乘向量机在说话人识别中的应用

说话人识别是语音识别的一种，是当前的研究热点之一。而基于统计学习理论的支持向量机（SVM）方法是一种新的机器学习算法，已成为机器学习研究的热点。讨论了一种改进的SVM即最小二乘向量机（LS-SVM）的方法进行说话人识别研究。研究表明，基于LS—SVM的说话人识别比传统的SVM说话人识别计算复杂度小、效率更高、对说话人识别有很强的适应性。     

融合PCA、LDA和SVM算法的人脸识别

为了提高人脸识别效率,提出了一种基于PCA、LDA和SVM算法融合的人脸识别方法。使用主成分分析（PCA）将人脸图像变换到新的特征空间中,消除图像特征间的相关性和噪声,提取人脸全局特征,在实验阶段取较多的投影方向使其尽可能多的保持原始信息;使用线性判别分析（LDA）算法进一步投影变换降低数据维度;使用支持向量机（SVM）分类识别。将PCA、LDA和SVM三种算法的优点结合起来,在ORL数据库上进行仿真实验,结果表明该方法的识别率可达99.0%。     

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.     

基于混合PCA和KFD的多级说话人确认

提出一种基于混合主成分分析(PCA)分类器和核Fisher判别(KFD)的多级说话人确认方法。利用PCA对注册说话人的特征向量进行降维,根据转换矩阵得到说话人特征向量的主成分空间和截断误差空间,结合这2个空间构造混合PCA分类器,用于快速判断最有可能的R个目标说话人,并采用KFD寻找最终目标说话人。仿真实验结果验证了该方法的有效性。     

基于新的MRSVM说话人辨识方法

提出一个新的基于MRSVM的说话人辨识方法,首先对语音特征矢量进行LDA降维,得到具有区分力的特征矢量,然后对其进行模糊核聚类,根据样本选择算法,选择聚类边界的特征矢量作为支持向量训练支持向量机,在不影响识别率的情况下,大大减少了支持向量机的存储量和训练量。实验表明该方法具有较好的总体效果。     

基于PCS-PCA分类器和支持向量机的说话人确认

针对语音识别率不高的问题,提出一种基于PCS-PCA和支持向量机的分级说话人确认方法.首先采用主成分分析法对话者特征向量降维的同时,得到说话人特征向量的主成份空间,在此空间中构造PCS-PCA分类器,筛选可能的目标说话人,然后采用支持向量机进行最终的说话人确认.仿真实验结果表明该方法具有较高的识别率和较快的训练速度.     

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HAAR小波的分级说话人辨识

从线性预测（LP）残差信号中提出了一种新的特征提取方法,这种特征跟单个的说话人的声道密切相关。通过把HAAR小波变换运用于LP 残差而获得了一个新的特征（HOCOR）。为了进一步提高系统的鲁棒性和辨识率,在采用分级说话人辨识的基础上,将基音周期的高斯概率密度对GMM分类器的似然度进行加权,形成新的似然度进行说话人辨识。试验结果显示,所提出系统的鲁棒性和辨识率都有所提高。     

Speaker identification based on normalized pitch frequency and Mel Frequency Cepstral Coefficients

This paper presents an efficient approach for automatic speaker identification based on cepstral features and the Normalized Pitch Frequency (NPF). Most relevant speaker identification methods adopt a cepstral strategy. Inclusion of the pitch frequency as a new feature in the speaker identification process is expected to enhance the speaker identification accuracy. In the proposed framework for speaker identification, a neural classifier with a single hidden layer is used. Different transform domains are investigated for reliable feature extraction from the speech signal. Moreover, a pre-processing noise reduction step, is used prior to the feature extraction process to enhance the performance of the speaker identification system. Simulation results prove that the NPF as a feature in speaker identification enhances the performance of the speaker identification system, especially with the Discrete Cosine Transform (DCT) and wavelet denoising pre-processing step.     

在噪声环境下的分级说话人辨识

将小波变换与维纳滤波结合起来对语音进行去噪.为了提高系统的鲁棒性和辨识率,在采用分级说话人辨识的基础上,将基音周期的高斯概率密度对GMM分类器的似然度进行加权,形成新的似然度进行说话人辨识.实验结果显示,所提出系统的鲁棒性和辨识率都有所提高.     

Text-independent speaker recognition based on the Hurst parameter and the multidimensional fractional Brownian motion model

In this paper, a text-independent automatic speaker recognition (ASkR) system is proposed-the SR/sub Hurst/-which employs a new speech feature and a new classifier. The statistical feature pH is a vector of Hurst (H) parameters obtained by applying a wavelet-based multidimensional estimator (M/spl I.bar/dim/spl I.bar/wavelets ) to the windowed short-time segments of speech. The proposed classifier for the speaker identification and verification tasks is based on the multidimensional fBm (fractional Brownian motion) model, denoted by M/spl I.bar/dim/spl I.bar/fBm. For a given sequence of input speech features, the speaker model is obtained from the sequence of vectors of H parameters, means, and variances of these features. The performance of the SR/sub Hurst/ was compared to those achieved with the Gaussian mixture models (GMMs), autoregressive vector (AR), and Bhattacharyya distance (dB) classifiers. The speech database-recorded from fixed and cellular phone channels-was uttered by 75 different speakers. The results have shown the superior performance of the M/spl I.bar/dim/spl I.bar/fBm classifier and that the pH feature aggregates new information on the speaker identity. In addition, the proposed classifier employs a much simpler modeling structure as compared to the GMM.     

Design of a real time automatic speech recognition system using Modified One Against All SVM classifier

In this paper, Texas Instruments TMS320C6713 DSP based real-time speech recognition system using Modified One Against All Support Vector Machine (SVM) classifier is proposed. The major contributions of this paper are: the study and evaluation of the performance of the classifier using three feature extraction techniques and proposal for minimizing the computation time for the classifier. From this study, it is found that the recognition accuracies of 93.33%, 98.67% and 96.67% are achieved for the classifier using Mel Frequency Cepstral Coefficients (MFCC) features, zerocrossing (ZC) and zerocrossing with peak amplitude (ZCPA) features respectively. To reduce the computation time required for the systems, two techniques – one using optimum threshold technique for the SVM classifier and another using linear assembly are proposed. The ZC based system requires the least computation time and the above techniques reduce the execution time by a factor of 6.56 and 5.95 respectively. For the purpose of comparison, the speech recognition system is also implemented using Altera Cyclone II FPGA with Nios II soft processor and custom instructions. Of the two approaches, the DSP approach requires 87.40% less number of clock cycles. Custom design of the recognition system on the FPGA without using the soft-core processor would have resulted in less computational complexity. The proposed classifier is also found to reduce the number of support vectors by a factor of 1.12–3.73 when applied to speaker identification and isolated letter recognition problems. The techniques proposed here can be adapted for various other SVM based pattern recognition systems.