CAREER: Understanding Real-World Auditory Scene Analysis

职业：了解现实世界的听觉场景分析

• 批准号: 1454094
• 负责人: Joshua McDermott
• 金额: $ 47.41万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2016-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454094&HistoricalAwards=false
• 关键词: CAREER; Understanding; Real; World; Auditory

A fundamental question in auditory science concerns how people can recognize speech and other sounds in the presence of competing sound sources, as when conversing with a dinner partner at a crowded restaurant. The process of hearing a sound of interest when it is embedded in a mixture of other sounds is known as "sound segregation" and human listeners vastly outperform machine systems for segregating sounds. However, the process is frequently effortful, is highly vulnerable to hearing impairment, including hearing impairment that typically accompanies normal aging. Understanding the basis of sound segregation in human listeners, and the factors that limit human segregation abilities, would enhance efforts to develop assistive listening devices and machine systems for robust speech recognition and sound recognition. The project will be complemented by an educational effort to stimulate interest in audition in the general public and in middle- and high-school students through a series of publicly available online video presentations describing auditory research with associated sound demonstrations.This CAREER award is aimed at enriching the understanding of human auditory perception by exploring the basis of sound segregation with natural sounds. The experiments will leverage recent advances in speech analysis and synthesis methods to 1) manipulate grouping cues in natural speech and test their effect on sound segregation in human listeners; 2) manipulate voice and speech structure to probe their role in segregation; and 3) test the ability of human listeners to attend to and track target sound sources. The long-term goals are to inspire signal-processing algorithms that facilitate segregation by human listeners and replicate their competence in machine systems.

听觉科学的一个基本问题是，人们如何在相互竞争的声源中识别语音和其他声音，就像在拥挤的餐馆里与同伴交谈一样。当一个感兴趣的声音被嵌入到其他声音的混合物中时，听到它的过程被称为“声音隔离”，人类听众在隔离声音方面远远优于机器系统。然而，这个过程往往是费力的，极易受到听力损伤的影响，包括通常伴随正常衰老的听力损伤。了解人类听者的声音隔离的基础，以及限制人类隔离能力的因素，将有助于开发辅助听力设备和机器系统，以实现强大的语音识别和声音识别。该项目将辅以一项教育工作，通过一系列公开的在线视频演示，描述听觉研究和相关的声音演示，激发公众和中学生对试听的兴趣。该奖项旨在通过探索声音与自然声音分离的基础，丰富对人类听觉感知的理解。这些实验将利用语音分析和合成方法的最新进展，1)操纵自然语音中的分组线索，并测试它们对人类听者声音分离的影响；2)操纵语音和言语结构，探究其在种族隔离中的作用；3)测试人类听者注意和追踪目标声源的能力。长期目标是激发信号处理算法，促进人类听众的分离，并在机器系统中复制他们的能力。