EAGER: Exploring the Role of Acoustic-Prosodic, Lexical, and Demographic Factors in Trustworthy Speech Perception for Conversational Agents

EAGER：探索声学韵律、词汇和人口统计因素在对话代理可信语音感知中的作用

• 批准号: 2332593
• 负责人: Sarah Levitan
• 金额: $ 9.81万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332593&HistoricalAwards=false
• 关键词: EAGER; Exploring; Role; Acoustic; Prosodic

This EArly Grant for Exploratory Research explores the role of acoustic-prosodic, lexical, and demographic factors in the perception of trustworthy synthesized speech for conversational agents. With advances in machine learning and speech technologies, conversational agents are becoming increasingly capable of engaging in human-like conversations. However, trust is crucial for effective communication and collaboration, and understanding the signals of trustworthy speech is essential for successful interactions. While researchers across disciplines have sought to discover the signals of trustworthy speech, mostly in human speech, there remains a gap between what is currently understood about trustworthy human speech and what can be implemented and used in conversational agents. This project will implement a series of innovative and exploratory perception studies designed to systematically investigate the prosodic, lexical, and demographic properties of trustworthy synthesized speech. To evaluate trust perception in contexts that require vulnerability and trust, real-world applications such as emotional support dialogues will be used. By uncovering the specific influences of acoustic-prosodic, lexical, and demographic factors, this research will advance our understanding of how trust is formed and maintained in human-machine interactions. The findings of this work will contribute valuable insights to improve the perceived trustworthiness of conversational agents. This, in turn, will enable the increased adoption of transformative technologies that will benefit society in important application areas, including assistive robot companions in homecare settings for the elderly and homebound, psychological assessment and treatment, and assistive medical care in hospitals. The main objective of this research is to identify acoustic-prosodic, lexical, and demographic factors in trust perception of synthetic speech. The project will systematically test the effects of these factors of synthesized speech on human trust using a large-scale crowdsourced perception study. Highly controlled parameters will be manipulated to test the effects of acoustic-prosodic features including pitch, intensity, and speaking rate, as well as lexical features such as dialogue act, politeness, and complexity. In addition, the study will examine individual differences in trust perception across speaker and listener traits. By exploring individual factors as well as interactive effects of combinations of prosodic, lexical and demographic factors, this research will provide a comprehensive understanding of their influence on user trust. The findings will inform the design, development, and deployment of conversational agents, leading to the creation of more trustworthy and engaging human-machine interactions.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.

这项探索性研究的早期赠款探讨了声学，词汇和人口因素在对会话代理的可信赖综合语音感知中的作用。随着机器学习和语音技术的进步，对话剂越来越有能力进行类似人类的对话。但是，信任对于有效的沟通和协作至关重要，了解可信赖的言语的信号对于成功互动至关重要。 尽管跨学科的研究人员试图发现可信赖的言语的信号，主要是在人类的言论中，但目前对可信赖的人言语的理解与可以在对话代理中实施和使用的内容之间存在差距。该项目将实施一系列创新和探索性的感知研究，旨在系统地研究可信赖的合成语音的韵律，词汇和人口特性。 为了评估需要脆弱性和信任的上下文中的信任感，将使用现实世界中的应用程序，例如情感支持对话。 通过揭示声学，词汇和人口统计学因素的特定影响，这项研究将促进我们对在人机相互作用中如何形成和维持信任的理解。 这项工作的发现将有助于有价值的见解，以提高对话代理人的可信赖性。 反过来，这将使人们能够越来越多地采用变革性技术，这些技术将使社会在重要的应用领域中受益，包括在老年人和家里的家庭护理环境中的辅助机器人同伴，心理评估和治疗，以及医院的辅助医疗服务。 这项研究的主要目的是确定综合语音信任感知的声学，词汇和人口统计学因素。该项目将使用大规模的人群感知研究系统地测试这些综合语音对人类信任的影响。将操纵高度控制的参数，以测试声学特征的影响，包括音高，强度和语言速率，以及词汇特征，例如对话行为，礼貌和复杂性。 此外，该研究将检查说话者和听众特征之间信任感知的个体差异。通过探索个人因素以及韵律，词汇和人口统计学因素组合的互动效果，这项研究将为他们对用户信任的影响提供全面的理解。这些发现将为对话代理的设计，开发和部署提供信息，从而导致创建更具值得信赖和引人入胜的人机互动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来评估的。