Objectively Quantifying Speech Outcomes of Children with Cleft Palate

客观量化腭裂儿童的言语结果

• 批准号: 9765280
• 负责人: Visar Berisha
• 金额: $ 22.55万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-16 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765280
• 关键词: 7 year old; Acoustics; Age; Algorithms; American; Articulation; Artificial Intelligence; Behavior Therapy; Child; Cleft Palate; Cleft lip with or without cleft palate; Clinic; Clinical; Clinical Research; Communities; Data; Databases; Development; Dimensions; Ear; Ensure; Environment; Evaluation; Exhibits; Four-dimensional; Frequencies; Funding; Gold; Human; Individual; International; Intervention; Judgment; Language; Learning; Maps; Measures; Methods; Modeling; National Institute of Dental and Craniofacial Research; Nose; Operative Surgical Procedures; Outcome; Outcomes Research; Output; Pathologist; Perception; Performance; Population; Positioning Attribute; Production; Protocols documentation; Proxy; Reference Values; Reporting; Research; Sampling; Series; Severities; Signal Transduction; Speech; Speech Acoustics; Speech Disorders; Technology; Time; Training; United States National Institutes of Health; Utah; Validation; Validity and Reliability; Visit; Work; base; cleft lip and palate; clinically relevant; craniofacial; impression; improved outcome; learning algorithm; mobile application; novel; predictive modeling; signal processing; success; tool

Perceptual assessment of hypernasality is considered a critical component when evaluating the speech of children with cleft lip and/or palate (CLP). However, most speech-language pathologists (SLPs) do not receive formal training for perceptual evaluation of speech and, as a result, research shows that the subjective ratings are inherently biased to the perceiver and exhibit considerable variability. In this project, we aim to develop an artificial intelligence (AI) algorithm that automatically evaluates speech along four dimensions deemed to be critically important by the Americleft Speech Outcomes Group (ASOG), namely speech acceptability, articulation, hypernasality, and audible nasal emissions. The AI algorithm in this project is based on an existing database of speech collected as a part of an NIH-funded project to develop reliable speech outcomes by improving the reliability of perceptual ratings by training clinicians (NIDCR DE019-01235, PI: Kathy Chapman). This database contains speech samples from 125 5-7 year olds along with multiple perceptual rating for each speech sample. The clinicians participating in this study were successfully trained using a new protocol from the Americleft Speech Outcomes Group and they exhibit excellent inter-clinician reliability. In SA1 we will develop an AI algorithm that automatically learns the relationship between a comprehensive set of speech acoustics and the average of the ASOG-trained expert ratings for each of the four perceptual dimensions. This approach is based on technology that the PIs have successfully used to evaluate dysarthric speech. Unique to these algorithms is modeling of perceptual judgments of trained experts using tools from statistical signal processing and AI. The output of the algorithms will map to a clinically- relevant scale, rather than to norm-referenced values that may or may not be meaningful. In SA2, we will evaluate the tool on new data by collecting new speech samples using a mobile app at a partner clinic using the same protocol as in the original study. Every collected sample will be further evaluated by ASOG trained clinicians. We will use this data to evaluate the accuracy of the AI model by comparing the model's predictions with the average of ASOG-trained experts. Preliminary results show promise that the proposed approach will yield a successful tool for accurately characterizing perceptual dimensions in the speech of children with CLP. These results indicate that a number of acoustic features that have been developed previously by the PIs accurately capture differences in hypernasality and articulation between the speech of three children with CLP (with varying severity). Furthermore, we show the success of our approach on a different, but related, task: objective evaluation of dysarthric speech. We show that an algorithm that automatically rates hypernasality performs on par with the judgment of human evaluators. The results of the proposed research will form the basis for a subsequent R01 proposal for the development and evaluation of a clinical tool to objectively quantify and track speech production in children with CLP.

在评估发音时，对鼻音过强的感知评估被认为是一个关键组成部分。 唇腭裂儿童（CLP）。然而，大多数言语语言病理学家（SLP）不接受 正式培训的感性评价的讲话，因此，研究表明，主观评级， 固有地偏向于感知者，并表现出相当大的可变性。在这个项目中，我们的目标是开发一个 一种人工智能（AI）算法，它自动评估语音沿着四个维度， 美国左派演讲结果组织（ASOG）的一个重要观点，即演讲的可接受性， 发音、鼻音过强和可听见的鼻排放。该项目中的AI算法基于现有的 作为NIH资助项目的一部分收集的语音数据库，通过以下方式开发可靠的语音结果： 通过培训临床医生提高感知评级的可靠性（NIDCR DE 019 -01235，PI：Kathy Chapman）。 该数据库包含来自125名5-7岁奥尔兹的语音样本，其中每个样本具有多个感知等级，沿着 语音样本。参与本研究的临床医生成功地接受了来自 Americleft Speech Outcomes Group，他们表现出出色的临床医生间可靠性。 在SA 1中，我们将开发一种AI算法，该算法可以自动学习 一套全面的语音声学和平均的ASOG训练的专家评级为每个 四个感知维度这种方法基于PI成功使用的技术， 评估构音障碍性言语。这些算法的独特之处在于对受过训练的专家的感知判断进行建模 使用统计信号处理和人工智能的工具。算法的输出将映射到临床上- 相关的规模，而不是正常参考值，可能会或可能不会有意义。在SA 2中，我们将 通过在合作伙伴诊所使用移动的应用程序收集新的语音样本，根据新数据评估工具， 与原始研究相同的方案。每个采集的样本将由经过培训的ASOG进行进一步评估 临床医生我们将使用这些数据，通过比较模型的预测来评估AI模型的准确性 与ASOG-trained专家的平均水平。初步结果表明，所提出的方法将 产生一个成功的工具，准确地表征知觉维度的儿童与CLP的讲话。 这些结果表明，一些声学特征，已开发的PI 准确捕捉三名CLP儿童言语中鼻音过重和发音清晰度的差异 (with不同的严重性）。此外，我们展示了我们的方法在不同但相关的任务上的成功： 构音障碍言语的客观评价。我们发现，一个算法，自动率hypernasality 与人类评估者的判断一致。拟议研究的结果将形成 为随后的R 01建议提供基础，以开发和评价临床工具， 量化和跟踪患有CLP的儿童的言语产生。