Speech variability in automatic speaker recognition systems for commercial and forensic purposes

Speaker recognition is a major task when security applications through speech input are needed. Nevertheless, speech variability is a main degradation factor in speaker recognition tasks. Both intra-speaker and external variability sources produce mismatch between training and testing phases. In this contribution, channel and inter-session variability are explored in order to accomplish real automatic systems for both commercial and forensic speaker recognition. Results are presented making use of "AHUMADA", a subset of "GAUDI" large speaker recognition-oriented database in Spanish.     

State-of-the-art in speaker recognition

Recent advances in speech technologies have produced new tools that can be used to improve the performance and flexibility of speaker recognition. While there are few degrees of freedom or alternative methods when using fingerprint or iris identification techniques, speech offers much more flexibility and different levels to perform recognition: the system can force the user to speak in a particular manner, different for each attempt to enter. Also, with voice input, the system has other degrees of freedom, such as the use of knowledge/codes that only the user knows, or dialectical/semantical traits that are difficult to forge. This paper offers an overview of the state-of-the-art in speaker recognition, with special emphasis on the pros and cons, and the current research lines. The current research lines include improved classification systems, and the use of high level information by means of probabilistic grammars. In conclusion, speaker recognition is far away from being a technology where all the possibilities have already been explored.     

Smart card information and operations using biometrics

Nowadays many systems need a portable media to store some sensible data, such as smart cards. The information can be protected by the user with his Personal Identification Number (PIN), or through biometrics. Unfortunately, there is not a smart card today that can verify the biometric template inside it, performing this task in the terminal. The author has developed the algorithms and data structures needed to solve this problem. Therefore, he has created a smart card with user biometric authentication, based on an Open Platform smart card (in this case, a JavaCard). To achieve these results, different biometric techniques have been studied: speaker verification, hand geometry and iris recognition. Experimental results are given to show the viability of the prototype developed     

Achieving a hands-free computer interface using voice recognitionand speech synthesis [for Windows-based ATE]

Software designers at Hill Air Force Base have developed a voice recognition and speech synthesis system (Voice Control) for use with the F-16 Analog Test Station Sustainment (FATSS) project. The Voice Control system is reliable, speaker independent, and has a total added hardware price tag of under $50.00 per station. In contrast to traditional voice recognition systems, operator training is not required. OO-ALC has developed a general-purpose internal interface (Voice Control) to the Speech Recognition and Text-To-Speech engines provided by Microsoft. Voice Control can be accessed by any 32-bit Windows software, which has windows messaging capability. This is available to standard programming languages such as LabWindows CVI, Borland or Microsoft C/C++, Visual Basic, or even commercial packages such as Lotus Notes and Microsoft Word. Through Voice Control, the computer uses both video and voice prompts to request input from the operator. The operator is allowed to enter data and to control the software flow by voice command or from the keyboard or mouse. The Voice Control system allows for dynamic specification of a grammar set, or legal set of commands. The use of a reduced grammar set greatly increases recognition accuracy. The computer voice enables the operator to focus his attention away from the computer screen, which is required for activities such as probing a circuit card and taking readings. When the operator takes readings, the computer, to insure reliable entry, echoes his voice entries. With electronic tuning, speech synthesis allows the operator to hear the resulting reading, enabling him to focus on the circuit card instead of constantly turning his head to see the computer screen. This paper describes the capability and functionality of the Voice Control system     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified     

润滑油金属磨粒的分类参数研究

 针对润滑系统中普遍存在的 8类金属磨粒,通过引入数字图像描述方法和处理技术,分别对磨粒的尺寸、形态、表面纹理以及颜色等参数进行了详细的研究,获取了各类磨粒识别的最佳参数,并提出了磨粒的分层识别策略,最后运用模糊 C-均值聚类方法实现了 8类磨粒的有效聚类。算例表明了本文方法的有效性。     

空间目标地基光电探测与识别技术的发展

空间目标地基光电探测与识别技术是空间态势感知的主要技术手段之一。首先归纳了空间目标的典型光学特征,分析了用于轨道与光学特征探测与识别的两类光电望远镜特点,梳理了国外空间目标光电探测与识别技术的发展历程,简要总结了空间目标地基光电探测与识别技术和应用需求间的主要问题,展望了空间目标光电探测与识别技术的未来发展方向。     

三维模型在SAR图像自动目标识别中的应用

提高高分辨率SAR图像在复杂战场环境中的目标识别能力,对防御未来战争中来自地面目标的威胁具有重要意义。针对地面特定目标的大小、方位、旋转等变化以及强杂波背景给目标识别带来的严重影响,提出将目标的三维模型投影到二维平面,采用余弦傅里叶矩和瑞利分布的CFAR检测方法分别对其矩特征和峰值特征进行提取,利用级联组合分类器对目标识别进行建模分析,并通过试验验证该方法的有效性。结果表明:该方法实现了在特征维数高和姿态变化下的目标识别,而且无需额外增加对制导控制系统的开销。     

铁路道口报警及控制系统

为了保证道口行车安全,在无人监护道口设置闪光和语音报警及道口横杆控制系统,利用单片机和无线通信技术以实现列车接近道口时,道口报警闪光器和语音扬声器同时报警,并放下道口横杆;列车离开道口时,道口闪光器和语音扬声器同时关闭,并升起道口横杆。在分析和比较几种单片机和传感器的基础上,确定本系统采用MCS51系列低功耗单片机和磁电传感器。     

基于脑电和眼动信号的人机交互意图识别

意图识别在人机交互（HCI）领域受到广泛关注,传统人机交互意图识别方法单纯依靠脑电（EEG）或眼动数据,不能很好地利用2种方法优点。为此,提出了一种融合脑电和眼动数据的人机交互意图识别方法,通过采集脑电和眼动信号,进行特征提取,输入机器学习模式识别网络进行意图识别,并基于Dempster-Shafer （D-S）证据理论进行决策层融合得出最终识别结果。招募了20名有效受试者进行交互意图识别实验,结果表明,基于脑电和眼动信号的人机交互意图识别方法识别准确率高于单纯依靠脑电和眼动数据的方法,可为下一步飞行器和武器系统人机交互系统自适应设计提供理论依据和技术支持。     

Automatic generation of binary feature detectors

The authors discuss the automatic generation of feature detectors, which is the major task in the design of classical pattern recognition systems. They present a software environment that, in place of human intuition, utilizes learning strategies and stochastic search procedures to guide the generation process. The environment allows the exploration of evolutionary learning processes and adaptive control mechanisms. A preliminary experiment with a two-class recognition system is described, and initial observations are discussed. The recognition task requires the classification of upper case English letters into two categories: target and nontarget     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements     

Image understanding research for automatic target recognition

Automatic target recognition (ATR) is an important capability for defense applications. Many aspects of image understanding (IU) research are traditionally used to solve ATR problems. The authors discuss ATR applications and problems in developing real-world ATR systems and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance the effectiveness of ATR-based weapon systems. They conclude that technological gains in developing robust ATR systems will lead to significant advances in many other areas of applications of image understanding     

图形背景理论模式下英语强调结构的教学认知

在英语中,说话人要重点突出的部分叫做强调.强调结构一直是学生学习英语的重点和难点。传统的教学方法存在诸多弊端,学生不会综合运用,不懂融会贯通。从新的视角对英语强调结构的教学进行认知势在必行。认知语言学中图形-背景理论为重新感知和理解强调结构提供了理据,进行形象具体化教学,有效提高学习效果。     

Comments on the Cost and Performance of Military Systems

Editor's Note: Mr. Fowler was the banquet speaker at the Tri Service Radar Symposium held at West Point in 1977. We invited Mr. Fowl to share his speech with the AES readership by providing this paper. The material and style correspond closely to his oral presentation.     

Data fusion in biometrics

Any biometric system has drawbacks and cannot warranty 100% identification rates, nor 0% false acceptance and rejection ratios. One way to overcome the limitations is through a combination of different biometric systems. In addition, a multimodal biometric recognition is more difficult to fool than a single, biometric system, because it is more unlikely to defeat two or three biometric systems than one. This paper summarizes the different data fusion levels, and how it must be performed in order to improve the results of each combined system on its own     

Multi-view space object recognition and pose estimation based on kernel regression

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.     

基于证据理论的模糊信息融合及其在目标识别中的应用

信息融合系统中的不确定性信息常常表现为模糊性和随机性信息。提出了一种在证据理论框架下实现模糊信息融合的方法。该方法首先基于随机集理论刻画模糊信息的隶属函数,获得了模糊观测下具有概然特性的似然函数,该似然函数表示在收集的模糊信息下确定为某一目标的可能性,在数值上表示了传感器信息对某一命题支持的程度,利用似然函数确定传感器输出的基本概率指派,最后利用Dempster-Shafer组合规则实现多传感器信息融合。     

基于SVM算法的虚假航迹识别

针对云雨杂波和主被动干扰导致多雷达传感器产生虚假目标航迹的问题,利用支持向量机(SVM)算法的自主学习能力,通过构建基于数据驱动的判别模型进行虚假航迹识别。针对航迹起始得到的目标潜在航迹,利用人工智能数据驱动、自学习的特点,设计了SVM算法。通过对已标记真假的目标航迹样本进行离线学习,形成虚假航迹识别的SVM分类器,实现了基于数据驱动的判别模型代替先验知识规则约束的固定模型,并在工程应用中,利用SVM分类器在线识别虚假航迹,完成实时剔除。通过实测雷达数据实验验证,该算法的目标虚假航迹准确率高达95%以上,完全满足实际的工程应用需求。相比基于阈值或规则进行硬性判断的传统虚假航迹识别方法,所提出的算法不仅提高了准确率,还具有较高的实时性,能够适应复杂多变的杂波环境,在实际应用中具有更强的适应性和实用性。因此,提出的基于SVM算法的虚假航迹识别方法对于密集杂波场景下的虚假航迹剔除问题具有显著的实际应用价值。