SBIR Phase I: A tool for estimating objective outcome measures in clinical speech applications

SBIR 第一阶段：用于评估临床语音应用中客观结果测量的工具

• 批准号: 1721483
• 负责人: Harry Schmitt
• 金额: $ 22.5万
• 依托单位: Aural Analytics
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1721483&HistoricalAwards=false
• 关键词: SBIR; Phase; tool; estimating; objective; SBIR; Phase; tool; estimating; objective

The broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase I project is to make uniformly high quality practice by Speech-Language Pathologists (SLPs) accessible to non-ambulatory and underserved rural populations. Because the inability to engage in spoken communication is among the most debilitating of all human conditions, poor access to high quality speech-language pathology services is an important societal issue. It creates significant health disparities affecting not only the quality of peoples' lives but also the economics of their communities. Because speech-language pathology treatment is highly behavioral and intensive, any proposed solution must improve both the performance and productivity of individual SLPs. To provide the necessary scalability required for a nation-wide problem, any implementation must include networked mobile tools as well as a strong algorithmic foundation that automatically generates objective, reliable, and sensitive outcome measures. The project has the potential for significant commercial impact. There are currently 160K SLPs in the US and that number expected to exceed 240K by 2024. Following a software-as-a-service model where clinicians freely download software to their mobile device but need active a subscription to access outcome ratings, projects to a total clinical market of between $20M and $100M.The proposed project addresses the strong demand for the development of dependent objective measures of pathological speech that exists in a variety of clinical settings. Currently, perceptual (subjective) ratings of speech conducted by clinicians are the gold standard for evaluating speech improvement or decline. While it is acknowledged that subjective ratings have poor validity and reliability, objective measures have not been readily available. This project takes a novel intellectual approach to the problem by building machine-learning algorithms that objectively model experts' subjective ratings, but with levels of reliability that far exceed that of clinicians. The computational engine takes as an input a set of speech samples from a speaker and automatically evaluates the speech along clinically-standard perceptual dimensions. This yields outputs that are immediately clinical interpretable, thereby exceeding the value of norm-based objective outcomes. The project goals are, (i) to refine and extend existing technology for the evaluation of pathological speech; and (ii) to rigorously evaluate its performance and utility using beta testing. Statistical analyses will determine whether the model outperforms the human ratings with respect to reliability and sensitivity, as has been the case in pilot tests.

小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是，通过语言病理学家（SLP）统一的高质量实践，可用于非公共和服务不足的农村人口。由于无法进行口头交流是所有人类条件中最令人衰弱的人之一，因此获得高质量的语音病理学服务的不良机会是一个重要的社会问题。它造成了重大的健康差异，不仅影响了人民生活的质量，而且影响其社区的经济学。由于语音语言病理治疗是高度行为和密集的，因此任何提议的解决方案都必须提高单个SLP的性能和生产力。为了提供全国性问题所需的必要可扩展性，任何实施都必须包括网络移动工具以及强大的算法基础，该基础自动生成客观，可靠和敏感的结果指标。该项目有可能产生重大商业影响。目前在美国有160k SLP，到2024年预计将超过240k。在软件即服务模型之后，临床医生将软件自由下载到其移动设备上，但需要主动订阅以访问结果等级，投资到总临床市场，向200万美元至100美元之间的总临床市场，提议的项目对依恋的量身定期的范围进行了批准，该项目的强劲需求涉及了依恋的范围。目前，临床医生进行的语音的感知（主观）评分是评估语音改善或下降的黄金标准。虽然公认的是主观评级的有效性和可靠性差，但客观措施尚未容易获得。该项目通过构建客观地对专家的主观评分来模拟的机器学习算法来采用一种新的智力方法，但可靠性远远超过了临床医生。该计算引擎作为输入一组来自扬声器的语音样本，并自动评估沿临床标准的感知维度的语音。这产生了立即临床解释的输出，从而超过了基于规范的目标结果的价值。项目目标是（i）完善和扩展现有技术以评估病理言论； （ii）使用Beta测试严格评估其性能和实用性。统计分析将确定模型在可靠性和敏感性方面是否表现出比率测试的情况是否优于人类评分。