Objectively Quantifying Speech Outcomes of Children with Cleft Palate

对鼻音过度的感觉评估被认为是评估语音的关键组成部分 唇腭裂儿童(CLP)。然而，大多数语音语言病理学家(SLP)并没有收到 正规训练对言语的感知评价，结果表明，研究表明主观评分 与生俱来地偏向于感知者，并表现出相当大的变异性。在这个项目中，我们的目标是开发一种 人工智能(AI)算法，自动评估语音沿四个维度被认为是 美国左派语音结果组织(ASOG)至关重要的是，即语音可接受性， 发音、过度鼻音和可听到的鼻音。本项目中的人工智能算法是基于现有的 作为美国国立卫生研究院资助的一个项目的一部分收集的语音数据库，该项目旨在通过 通过培训临床医生提高知觉评分的可靠性(NIDCRDE019-01235，PI：Kathy Chapman)。 这个数据库包含125个5-7岁儿童的语音样本，以及每个人的多个感知评级 语音样本。参与这项研究的临床医生成功地接受了使用新方案的培训 American Left语音结果组，他们表现出极好的临床医生间可靠性。 在SA1中，我们将开发一种人工智能算法，该算法自动学习 一套全面的语音声学和每个ASOG培训的专家平均评级 四个感性维度。此方法基于PI已成功使用的技术 评估发音障碍。这些算法的独特之处在于对训练有素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- 相关的比例尺，而不是可能有意义或没有意义的常模参考值。在SA2中，我们将 通过在合作伙伴诊所使用移动应用程序收集新的语音样本来评估该工具的新数据 与原始研究中的相同方案。每个采集的样本都将由ASOG培训人员进行进一步的评估 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 与接受过ASOG培训的专家的平均水平持平。初步结果表明，提出的方法将有望 为准确描述CLP儿童言语中的知觉维度提供了一个成功的工具。 这些结果表明，PI以前开发的一些声学特征 准确捕捉三例慢性阻塞性肺病儿童在上鼻音和发音方面的差异 (严重程度各不相同)。此外，我们还展示了我们的方法在一项不同但相关的任务上的成功： 目的评价构音障碍患者的言语功能。我们展示了一种自动对鼻音过大进行评级的算法 表现与人类评估者的判断不相上下。建议的研究结果将形成 为随后的R01提案提供基础，以开发和评估客观的临床工具 量化和跟踪患有CLP的儿童的言语产出。

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.