Collaborative Research: Separating Speech from Speech Noise to Improve Intelligibility

合作研究：将语音与语音噪声分离以提高清晰度

• 批准号: 0534841
• 负责人: Pierre Divenyi
• 金额: --
• 依托单位: East Bay Institute for Research and Education Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-15 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534841&HistoricalAwards=false
• 关键词: Collaborative; Research; Separating; Speech; Noise

Separating signals that have been mixed together is an archetypal engineering probelm. The past decade has seen the emergence of a several approaches applicable to separating sound mixtures -- for example, a restaurant scenario in which a desired target voice must be extracted from the background babble of other patrons. However, the most appropriate goal, and hence the way to measure performance, is not always clear. In this project, the goal is established as improving intelligibility i.e. processing sound mixtures so a human listener can better understand what can be said. This requires a collaboration between computer science/electrical engineering -- to provide the separation algorithms -- and auditory scientists/psychologists -- to guide the results towards perceptually-relevant improvements, and to evaluate the results in listener tests.The particular techniques to be developed and combined include blind source separation (such as independent component analysis), computational auditory scene analysis (simulations of what is understood about human perceptual processing), and model-driven approaches derived from the machine-learning techniques of speech recognition. One specific area of interest is the synthesis of `minimally-informative noise', acoustic tokens that effectively communicate both what can be inferred and what remains unknown about the target signal, and which can leverage the powerful perceptual inference of human listeners.This project will lead to implementations of acoustic signal separation that deliver the greatest benefit to human listeners, potentially including both normal-hearing and hearing-impaired individuals. This has a broad range of applications from processing archival recordings through to improved real-time communications technologies, as well as the potential to help automatic speech recognition systems.

分离混合在一起的信号是一个典型的工程问题。在过去的十年中，出现了几种适用于分离混合声音的方法——例如，在一个餐厅场景中，必须从其他顾客的背景语声中提取出所需的目标声音。然而，最合适的目标以及衡量性能的方法并不总是明确的。在这个项目中，目标是提高可理解性，即处理声音混合，以便人类听众可以更好地理解所说的内容。这需要计算机科学/电子工程（提供分离算法）和听觉科学家/心理学家（指导结果朝着感知相关的改进方向发展）之间的合作，并在听众测试中评估结果。需要开发和结合的特定技术包括盲源分离（如独立分量分析），计算听觉场景分析（模拟人类感知处理的理解），以及源自语音识别机器学习技术的模型驱动方法。我们感兴趣的一个特定领域是“最小信息噪声”的合成，这是一种声学标记，可以有效地传达目标信号的可推断和未知内容，并且可以利用人类听众的强大感知推断。这个项目将导致实现声信号分离，为人类听众带来最大的好处，可能包括正常听力和听力受损的人。这具有广泛的应用范围，从处理档案记录到改进的实时通信技术，以及帮助自动语音识别系统的潜力。