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; 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.

在构建安全和私人计算系统中，使用移动设备来验证一个人的身份，无论是用于访问设备本身，还是作为验证访问其他附近设备的访问的平台，都是一个重要的问题。 该提案旨在通过开发人们的声音模型来改善语音识别作为一种身份验证工具，这些模型会独特地影响单个人的发出声音。 这些新型的生物特征性状将使用许多移动设备上存在的多种传感器来捕获和推断，并研究了它们的潜力，以既可以唯一地识别个体并实际上在真实的环境中使用。 这项工作还包括对设备特征，用户行为和用户的物理状态（例如感冒）的差异的系统研究，同时会影响语音生产以及对其进行建模并将其用作生物特征识别标识符的能力，以及如何推断语音特征的方法可以解决这些差异。 这项工作将为语音生产科学以及有关利用物理系统的独特特征，潜在的实用应用以及支持本科和研究生教育的机会提供科学贡献。拟议的研究表明，如何探索人类的生理和移动传感以增强移动身份验证。 围绕物理语音生产建模的工作将集中于对人声道中不同地方的不同声音的音素声音起源进行建模。 单个生理学上的这些差异类似于类似的想法，这些想法在计算设备的物理特征中使用微小的变化来为每个设备生成一个独特的基于硬件的签名。 将通过信号处理算法来感知它们，这些算法利用了来自多个麦克风的声音捕获时间差异，并可以单独评估并与其他生物识别特征进行评估，这些特征是身份验证质量的其他生物识别特征，这些特征是通过不同尺寸数据集的错误率来衡量的。 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。 其中包括各种设备的麦克风放置和音频芯片组以及采样率；一个人的抓地力和与设备的互动及其相对位置的各个方面，以及他们的姿势和运动；以及它们的生理学（即疾病）和心理状态的方面（通过视频引起情感的标准技术）。 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，并评估潜在利用统计学习方法非常适合相对数据值，而不是相关分析（例如相关分析），例如相关数据值（例如，高斯混合物）来解决这些奖励，以解决NSF的构建范围。和更广泛的影响审查标准。