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.

使用移动的设备来认证个人的身份，既用于访问设备本身，又作为用于验证对其他附近设备的访问的平台，是在构建安全和私有计算系统中要解决的重要问题。 该提案旨在通过开发人的声道的物理模型来改进语音识别作为认证工具，该声道独特地影响个人如何产生声音。 这些新的生物特征将被捕获和推断使用各种传感器，目前在许多移动的设备，并研究其潜力，以唯一地识别个人和实际使用在真实的背景。 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建模并将其用作生物识别符的能力，以及推断语音特征的方法如何解释这些差异。 这项工作将为语音产生科学和更一般的问题做出科学贡献，这些问题涉及利用物理系统的独特特性，认证中的潜在实际应用以及支持本科和研究生教育的机会。拟议的研究演示了如何人体生理学和移动的传感可以探索，以提高移动的身份验证。 围绕建模物理语音产生的工作将集中在对来自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使用计算设备的物理特性的微小变化来为每个设备生成唯一的基于硬件的签名的类似想法。 它们将通过信号处理算法来感知，这些算法利用多个麦克风捕获声音的时间差，并单独评估和与其他生物特征结合评估认证质量，如通过不同大小数据集的错误率测量的。 下一阶段的工作将研究捕获的上下文如何影响生物识别质量。 这些包括麦克风放置和音频芯片组以及各种设备的采样率;人的抓握和与设备的交互的方面以及设备与他们的嘴的相对位置，以及他们的姿势和运动;以及他们的生理方面（即，疾病）和心理状态（通过用于通过视频引发情感的标准技术）。 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，以及评估利用统计学习方法的潜力-适用于相对而非绝对的数据值，如相关分析和高斯混合模型，以解决这些背景变化。该奖项反映了NSF的法定使命，并被认为是值得的通过使用基金会的知识价值和更广泛的影响审查标准进行评估来提供支持。