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: 10268203
• 负责人: Eric Martin Johnson
• 金额: $ 7.01万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10268203
• 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.

项目摘要/摘要 八分之一的美国人有听力损失，而这一征兵是重大的健康和经济负担 （Blackwell等，2014年）。 有背景噪声时（见Dillon，2012年）。 近年来，它们在嘈杂的环境中仍然几乎没有好处。 尽管大量数量 大学和HA公司的研究发生了变化。 在HI Liscens的噪声中可以理解的单微粒算法的演示（Health等， 2013年，2014年，2015年）。 听众可以提高从地板到天花板水平的理解力） 因此，不适合将其植入和耳蜗植入（顺式）。 因此，是一种能够实时运行的高效降噪算法。 旨在满足这一关键需求。 当前建议的项目的长期目标是减轻听众的主要听力 障碍，这是差异的理解背景噪声中的语音。 基于一种新颖的基础方案，算法旨在为任何HI提供可观的好处 实时的听众。 通信应用。 正常听力（NH）听众。 少量的将来的时间帧信息可能会降噪的性能，但会 引入简短的处理延迟。 面对面的通信设备（具有，顺式等）具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。 这些需求中的时框信息（最多150毫秒）将带来更好的语音智能手段。 但是，目前将建立任何潜在收益的幅度。 使用HI和NH听众，我们将测量已处理过的嘈杂句子的智能手段 使用各种未来的时间信息。 这个全面的奖学金培训计划将提供来自 世界一流的教师在高度支持和生产的环境中。 申请人具有过渡到其研究职业下一阶段所需的技能，改变了我们的对待 听力损失，并严重影响数百万美国人的生活质量。