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

小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是使言语语言病理学家（SLP）的统一高质量实践可用于卧床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具破坏性的，因此难以获得高质量的言语语言病理学服务是一个重要的社会问题。它造成了巨大的健康差距，不仅影响到人们的生活质量，而且影响到其社区的经济。由于言语-语言病理学治疗是高度行为化和密集化的，因此任何提出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联网的移动的工具以及强大的算法基础，以自动生成客观，可靠和敏感的结果测量。该项目有可能产生重大的商业影响。目前美国有16万台SLP，预计到2024年将超过24万台。遵循软件即服务的模式，临床医生免费下载软件到他们的移动终端，但需要积极订阅访问结果评级，项目的总临床市场之间的20 M和1亿美元。拟议的项目解决了强烈的需求，发展依赖客观措施的病理性语言，存在于各种临床设置。目前，由临床医生进行的语音的感知（主观）评级是评估语音改善或下降的金标准。虽然人们承认，主观评级的有效性和可靠性较差，但客观的衡量标准并不容易获得。该项目通过构建机器学习算法来解决这个问题，该算法客观地模拟专家的主观评级，但其可靠性水平远远超过临床医生。计算引擎将来自说话者的一组语音样本作为输入，并沿着临床标准感知维度自动评估语音沿着。这产生了临床上可立即解释的输出，从而超过了基于规范的客观结果的价值。该项目的目标是，（一）完善和扩展现有的技术，用于评估病理性语言;和（二）严格评估其性能和效用使用beta测试。统计分析将确定该模型在可靠性和敏感性方面是否优于人类评级，就像试点测试中的情况一样。

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.