Real-time deep learning to improve speech intelligibility in noise

实时深度学习提高噪声中的语音清晰度

• 批准号: 10558196
• 负责人: Eric Martin Johnson
• 金额: $ 0.25万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10558196
• 关键词: Address; Algorithms; American; Area; Auditory; Cellular Phone; Characteristics; Cochlear Implants; Communication; Complex; Data; Devices; Diagnosis; Economic Burden; Effectiveness; Environment; Equilibrium; Etiology; Faculty; Fellowship; Floor; Foundations; Future; Goals; Healthcare; Hearing; Hearing Aids; Human; Implant; Measures; Mentors; Mission; National Institute on Deafness and Other Communication Disorders; Noise; Performance; Phase; Prevention; Process; Quality of life; Recommendation; Research; Research Personnel; Research Training; Scheme; Seminal; Signal Transduction; Speech; Speech Intelligibility; Strategic Planning; System; Telephone; Testing; Time; Training; Translating; Universities; Videoconferencing; Work; artificial neural network; base; career; communication device; deep learning; deep neural network; design; experimental study; health economics; hearing impairment; hearing loss treatment; improved; microphone; network architecture; neural network; normal hearing; novel; novel strategies; operation; skills; speech in noise; wearable device

Project Summary/Abstract One in eight Americans has hearing loss, and this constitutes a major health and economic burden (Blackwell et al., 2014). The primary complaint of hearing-impaired (HI) listeners is difficulty understanding speech when background noise is present (see Dillon, 2012). While hearing aids (HAs) have improved in recent years, they still provide little benefit in noisy environments. For decades, a means of improving the ability to understand speech in background noise appeared unattainable, despite substantial amounts of research by both universities and HA companies. This changed when deep learning provided the first demonstration of a single-microphone algorithm that improves intelligibly in noise for HI listeners (Healy et al., 2013, 2014, 2015). Although this algorithm provides massive intelligibility improvements (even allowing listeners to improve intelligibility from floor to ceiling levels), it is currently not implemented to operate in real time and is therefore not suitable for implementation into HAs and cochlear implants (CIs). What is needed, therefore, is a highly effective noise-reduction algorithm that is capable of operating in real time. This project aims to address this critical need. The long-term goal of the currently proposed project is to alleviate HI listeners’ predominant hearing handicap, which is difficulty understanding speech in background noise. The first aim introduces a new algorithm, based on a novel foundational scheme, that is designed to provide substantial benefit for any HI listener in real time. This algorithm will be well suited for implementation into HAs, CIs, and other face-to-face communication applications. The effectiveness of this new algorithm will be quantified using both HI and normal-hearing (NH) listeners. The second aim expands upon this new algorithm by modifying it to accept a small amount of future time-frame information, which could improve its noise-reduction performance but will introduce a brief processing delay. The rationale is that different devices have different allowable latencies. Face-to-face communication devices (HAs, CIs, etc.) have strict low-latency requirements, but other important communication systems (e.g., telephones) have different requirements. It is possible that the addition of future time-frame information within these requirements (up to 150 ms) will result in even better speech intelligibility. But the magnitude of any potential benefit is unknown. This critical information will be established currently. Using both HI and NH listeners, we will measure intelligibility for noisy sentences that have been processed using various amounts of future time information. This comprehensive fellowship training plan will provide individualized, mentored research training from world-class faculty in a highly supportive and productive environment. The proposed work will endow the applicant with the skills needed to transition to the next stage of his research career, transform our treatment of hearing loss, and substantially impact quality of life for millions of Americans.

项目总结/摘要 八分之一的美国人有听力损失，这构成了主要的健康和经济负担 （Blackwell等人，2014年）。听障听众的主要抱怨是难以理解 当存在背景噪声时的语音（参见Dillon，2012）。助听器（HAs）的发展 近年来，它们在嘈杂的环境中仍然没有什么好处。几十年来，一种改善 在背景噪音中理解语音的能力似乎是无法达到的，尽管有大量的 大学和HA公司的研究。当深度学习提供了第一个 演示了单麦克风算法，其改善了HI收听者的噪声的可理解性（Healy等人， 2013年，2014年，2015年）。尽管该算法提供了大量的可理解性改进（甚至允许 收听者以提高从地板到天花板水平的可懂度），但是目前还没有实现在真实的环境中操作 因此不适合在HA和人工耳蜗（CI）中实施。所需要的， 因此是一种能够在真实的时间内操作的高效降噪算法。这个项目 旨在满足这一关键需求。 目前建议的计划的长远目的，是减轻听障人士的听觉负担 障碍，即在背景噪音中难以理解语音。第一个目标介绍了一个新的 算法，基于一种新的基础方案，旨在为任何HI提供实质性的好处 真实的听众。该算法将非常适合于在HA、CI和其他面对面 通信应用。这种新算法的有效性将使用HI和 听力正常（NH）的听众。第二个目标扩展了这个新的算法，修改它接受一个 少量的未来时间帧信息，这可以提高其降噪性能，但将 引入短暂的处理延迟。其基本原理是不同器械具有不同的允许延迟。 面对面通信设备（HA、CI等）有严格的低延迟要求，但其他重要的 通信系统（例如，电话）有不同的要求。未来可能会增加 在这些要求（高达150 ms）内的时间帧信息将导致甚至更好的语音清晰度。 但任何潜在好处的大小都是未知的。这一关键信息将在目前得到确定。 使用HI和NH监听器，我们将测量经过处理的嘈杂句子的可懂度 使用不同数量的未来时间信息。 这项全面的研究金培训计划将提供个性化的，指导性的研究培训， 世界一流的教师在一个高度支持和富有成效的环境。拟议的工作将赋予 申请人具有过渡到他的研究生涯的下一阶段所需的技能，改变我们的治疗方法， 听力损失，并严重影响数百万美国人的生活质量。