元音到舌头倒置的AURORA模型：教学与临床应用
AURORA Model of Formant-to-Tongue Inversion for Didactic and Clinical Applications
AI总结：
AURORA模型通过前两个共振峰预测元音发音时的舌头位移与形状，为教学和生物反馈应用提供理论基础，结合超声成像与语音数据，开发了Shiny应用和实时生物反馈软件。
摘要：
本文阐述了AURORA模型的概念和计算基础。AURORA模型基于前两个共振峰预测元音发音时的舌头位移和形状，旨在作为教学工具，帮助解释共振峰与底层发音之间的关系，以及为生物反馈应用提供理论基础。该模型受到超声舌成像和40名英语母语者语音数据的启发。本文讨论了模型的动机、建模目标以及模型架构。我们对模型进行了定性评估，重点在于选定的舌头特征。随后，我们介绍了两个工具，以使该模型更易于更广泛的受众使用，即Shiny应用和实时舌头生物反馈原型软件。潜在用户包括语音学学生、与语音学相关的语言学家，以及言语和语言治疗师及患者。
英文摘要：
This paper outlines the conceptual and computational foundations of the AURORA (Acoustic Understanding and Real-time Observation of Resonant Articulations) model. AURORA predicts tongue displacement and shape in vowel sounds based on the first two formant values. It is intended as a didactic aid helping to explain the relationship between formants and the underlying articulation, as well as a foundation for biofeedback applications. The model is informed by ultrasound tongue imaging and acoustic data from 40 native speakers of English. In this paper we discuss the motivation for the model, the modelling objectives as well as the model architecture. We provide a qualitative evaluation of the model, focusing on selected tongue features. We then present two tools developed to make the model more accessible to a wider audience, a Shiny app and a prototype software for real-time tongue biofeedback. Potential users include students of phonetics, linguists in fields adjacent to phonetics, as well as speech and language therapy practitioners and clients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
