RI: Medium: Collaborative Research: Understanding Individual-Level Speech Variability: From Novel Articulatory Data to Robust Speaker Recognition

语言是人类独有的能力。声道是人类普遍使用的乐器，在言语产生过程中以极大的灵活性和技巧来传达丰富的语言和非语言信息。 该项目将使人们能够从根本上了解由于身体发声乐器的形状和大小的差异，个人在语音清晰度方面的差异。了解人们在语音产生方面的差异有助于改进自动说话人识别，这对国家安全至关重要。 该项目可以为所有人口（包括儿童、老年人和非母语者）提供强大的基于语音的访问技术设计。该项目的结果还有助于更好地理解和治疗疾病（例如，唇裂/腭裂），疾病（例如，头颈部癌症、呼吸暂停）或损伤，其中人类语言清晰度受到影响。来自200名个人的新成像数据，以及由跨学科团队创建的相关工具，注释和解释将与科学界广泛分享。该项目将为学生提供一个独特的研究培训机会，在综合语音科学和技术。该项目的总体目标是促进对声道形态和语音清晰度如何相互作用的科学理解，并解释说话者语音信号特性的变化和不变方面。特别是科学的兴趣是发音策略的性质所采取的个人在存在的结构差异，在他们之间实现语音对等。同样感兴趣的是声道形态差异的哪些方面以及如何反映在声学语音信号中，以及这些差异是否可以从语音声学中估计。这一目标的一个关键部分是创建正向和反向计算模型，将声道细节与语音声学联系起来，以揭示个体扬声器的差异，并为鲁棒的扬声器识别技术的设计提供信息。该项目超越了最先进的方法，专注于使用新的成像技术和计算建模直接调查动态人类声道，以阐明声道结构中的扬声器间变异性，以及语言清晰度的实现策略。使用我们帮助开发的具有整个移动声道的上级空间分辨率的新型磁共振成像（具有出色时间分辨率的动态实时2D和加速体积3D），该项目将收集和量化来自160个美国本土英语的语音产生的时空细节，涵盖北美主要方言地区和40个非母语人士。研究结构（形状和大小）与功能（声道形成的动力学及其声学后果）之间相互作用的实验、理论和方法学方法可以带来新的理论进步，改善语言单位的语音特征，这些特征在说话者中普遍存在。它还提供了解释个人特定语音模式的能力，可以提高对科学基础的理解，并创建强大的自动说话人识别技术，不仅能够通过采用新的说话人相关特征来确定两个说话人是不同的，而且还可以通过分析生物启发的结构和发音细节来确定他们是如何以及为什么不同的。

Speech is a unique human capability. The vocal tract is the universal human instrument played with great dexterity and skill in the production of speech to convey rich linguistic and paralinguistic information. The project will enable fundamental understanding of how individuals differ in their speech articulation due to differences in shape and size of their physical vocal instrument. Knowledge of how people differ in their speech production can help create improved automatic speaker recognition, technologies important for national security. The project can inform design of technologies for robust speech-based access for all members of the population, including children, the elderly, and non-native speakers of a language. Results from the project can also assist in better understanding and treating disorders (e.g., cleft lip/palate), illness (e.g., head and neck cancer, apnea) or injury where human speech articulation is affected. The novel imaging data from 200 individuals, and associated tools, annotations and interpretations created by the interdisciplinary team will be shared broadly with the scientific community. The project will provide a unique research training opportunity for students in integrated speech science and technology. The overarching goal of this project is to advance scientific understanding of how vocal tract morphology and speech articulation interact and explain the variant and invariant aspects of speech signal properties across talkers. Of particular scientific interest is the nature of articulatory strategies adopted by individuals in the presence of structural differences across them to achieve phonetic equivalence. Equally of interest are what aspects of, and how, vocal tract morphological differences are reflected in the acoustic speech signal, and if those differences can be estimated from speech acoustics. A crucial part of this goal is to create forward and inverse computational models that relate vocal tract details to speech acoustics toward shedding light on individual speaker differences and informing design of robust speaker recognition technologies. This project goes beyond state-of-the-art methods by focusing on direct investigation of the dynamic human vocal tract using novel imaging techniques and computational modeling to illuminate inter-speaker variability in vocal tract structure, as well as the strategies by which linguistic articulation is implemented. Using novel Magnetic Resonance Imaging with superior spatial resolution of the entire moving vocal tract that we helped develop (dynamic realtime 2D with excellent temporal resolution and accelerated volumetric 3D), the project will gather and quantify spatio-temporal details of speech production from 160 native American English covering the major dialectal regions of North America and 40 non-native speakers. The experimental, theoretical, and methodological approaches investigating the interplay between structure (shape and size) and function (dynamics of vocal-tract shaping and its acoustic consequences) can lead to new theoretical advances with improved phonetic characterizations of linguistic units that are general across speakers. It also offers the ability to explain individual specific speech patterns that can improve both understanding the scientific underpinning and creating robust automatic speaker recognition technology, enabling to determine not only that two talkers are different by the adoption of novel speaker dependent features, but also how and why they differ, by analyzing biologically-inspired details of structure and articulation.