EAGER: Exploring the Feasibility of Phoneme Sound Origins to Enhance Mobile Authentication

EAGER：探索音素声音起源增强移动认证的可行性

• 批准号: 1835963
• 负责人: Jie Yang
• 金额: $ 15万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835963&HistoricalAwards=false
• 关键词: EAGER; Exploring; Feasibility; Phoneme; Sound

Using mobile devices to authenticate a person's identity, both for access to the device itself and as a platform for verifying access to other nearby devices, is an important problem to address in building secure and private computing systems. This proposal seeks to improve voice recognition as an authentication tool by developing physical models of people's vocal tracts that uniquely affect how individual people produce sounds. These novel biometric traits will be captured and inferred using a variety of sensors that are present on many mobile devices, and studied for their potential to both uniquely identify individuals and be practically used in real contexts. The work also includes systematic studies of how differences in device characteristics, user behavior, and users' physical state (for instance, having a cold) affect both voice production and the ability to model it and use it as a biometric identifier, and how methods for inferring voice characteristics can account for these differences. The work will lead to scientific contributions to both the science of voice production and more general questions about leveraging unique characteristics of physical systems, potential practical applications in authentication, and opportunities to support both undergraduate and graduate education. The proposed research demonstrates how human physiology and mobile sensing can be explored to enhance mobile authentication. The work around modeling physical voice production will focus on modeling the phoneme sound origin for different sounds from different places in the human vocal tract. These differences in individual physiology are analogous to similar ideas that use small variations in the physical characteristics of computing devices to generate a unique hardware-based signature for each device. They will be sensed through signal processing algorithms that leverage time differences in sound capture from multiple microphones and be evaluated both individually and in combination with other biometric features on the quality of authentication as measured by error rates across datasets of different sizes. The next phase of the work will examine how the context of capture affects the biometric quality. These include the microphone placement and audio chipset and sampling rates of a variety of devices; aspects of a person's grip and interaction with the device and its relative location to their mouth, as well as their posture and motion; and aspects of their physiological (i.e., sickness) and psychological state (through standard techniques for eliciting emotion through video). Finally, to address the problem of adapting models across contexts, the project team will develop techniques to sense pose, distance, and emotional state, as well as evaluate the potential to leverage statistical learning methods well-suited to relative rather than absolute data values such as correlation analysis and Gaussian Mixture Models to address these contextual variations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

使用移动设备来验证个人身份，无论是为了访问设备本身还是作为验证对附近其他设备的访问的平台，是构建安全和私有计算系统时需要解决的一个重要问题。 该提案旨在通过开发人们声道的物理模型来改进语音识别作为一种身份验证工具，这些模型对个人发出声音的方式有独特的影响。 这些新颖的生物识别特征将使用许多移动设备上存在的各种传感器来捕获和推断，并研究它们在唯一识别个体和在真实环境中实际使用的潜力。 这项工作还包括系统研究设备特征、用户行为和用户身体状态（例如感冒）的差异如何影响语音产生以及对其进行建模并将其用作生物识别标识符的能力，以及方法如何推断语音特征可以解释这些差异。 这项工作将为语音产生科学以及有关利用物理系统的独特特征、身份验证中的潜在实际应用以及支持本科生和研究生教育的机会等更普遍的问题做出科学贡献。拟议的研究展示了如何探索人类生理学和移动传感来增强移动身份验证。 围绕物理语音生成建模的工作将重点是对人类声道中不同位置的不同声音的音素声音起源进行建模。 个体生理学的这些差异类似于类似的想法，即利用计算设备物理特性的微小变化为每个设备生成独特的基于硬件的签名。 它们将通过信号处理算法进行感知，该算法利用多个麦克风捕获声音的时间差，并单独或与其他生物识别特征结合评估身份验证的质量，通过不同大小的数据集的错误率来衡量。 下一阶段的工作将研究捕获环境如何影响生物识别质量。 其中包括麦克风的放置和音频芯片组以及各种设备的采样率；人对设备的抓握和交互、设备与嘴的相对位置以及姿势和运动等方面；以及他们的生理（即疾病）和心理状态（通过视频引发情感的标准技术）的各个方面。 最后，为了解决跨环境调整模型的问题，项目团队将开发感知姿势、距离和情绪状态的技术，并评估利用非常适合相对而非绝对数据值的统计学习方法的潜力，例如作为相关分析和高斯混合模型来解决这些背景变化。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。