The effects of telepractice technology on dysarthric speech evaluation

远程治疗技术对构音障碍言语评估的影响

• 批准号: 10196408
• 负责人: Visar Berisha
• 金额: $ 23.01万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10196408
• 关键词: Acoustics; Acquired Immunodeficiency Syndrome; Algorithms; Area; Articulation; Clinic Visits; Clinical; Code; Computer software; Data; Data Compression; Diagnosis; Dimensions; Dysarthria; Evaluation; General Population; Instruction; Internet; Language; Measurement; Measures; Morphologic artifacts; Noise; Outcome; Pathologist; Patients; Persons; Phonation; Sampling; Scheme; Signal Transduction; Source; Speech; Speech Intelligibility; Speech Sound; Statistical Models; Tablets; Technology; Telecommunications; Teleconferences; Testing; Voice; base; clinically relevant; impression; improved; smartphone Application; transmission process; vector

Project Summary/Abstract With telepractice becoming an increasingly popular, indeed necessary, alternative to clinic visits, speech- language pathologists (SLPs) evaluate speech that is audio captured and transmitted remotely via a teleconferencing solutions (e.g. Zoom). All popular teleconferencing applications use speech compression algorithms based on linear predictive coding (LPC) to reduce bandwidth required for speech transmission. LPC compression algorithms decompose speech into phonatory source and articulatory filter parameters, which are then independently vector quantized and temporally smoothed based on schemes that have been developed and optimized for compressing speech sampled from the general population. In this way, speech is transmitted with the least number of audible artifacts and the highest level of intelligibility for the given internet connection constraints. Because LPC algorithms are optimized using large corpora of typical speech, they are fundamentally not well-suited for faithfully transmitting the amount of distortion or noise commonly present in the dysarthric speech signal, and articulation and voice features are particularly vulnerable to corruption. In this proposal the aim is to systematically characterize the impact of speech compression algorithms commonly used in telepractice platforms on speech intelligibility, perceptual evaluation, and acoustic measurement. This is done via two aims: SA1: Evaluate the effects of teleconferencing speech compression algorithms at three internet bandwidth levels on the perceptual and acoustic assessment of dysarthric speech Existing high-fidelity audio recordings of words and sentences and sustained phonations from 20 speakers with various dysarthrias will be encoded at three compression rates to simulate low-, moderate-, and high- bandwidth internet connectivity. Twenty SLPs will transcribe the samples to attain intelligibility measures for the original and encoded words and sentences; and perceptually rate vocal quality on sustained phonations. Acoustic measures of articulation and voice will be extracted. Within-subject statistical models will evaluate impact of bandwidth condition on perceptual and acoustic outcomes within speech tasks. SA2: Compare outcomes for dysarthric speech recorded in a telepractice session (compressed) versus that recorded simultaneously in-person via smartphone application (uncompressed). Fifteen speakers with dysarthria will participate in simulated telepractice speech assessments administered by SLPs. Subsequently, recordings from session (compressed) and simultaneously recorded in-person samples (uncompressed) will be scored by SLPs. In-person recordings will also be compressed as in SA1 conditions. Acoustic metrics will be extracted from all samples. Within-subject statistical models will evaluate sample differences across conditions (uncompressed, compressed during telepractice, and low- moderate- and high- bandwidth compression levels). Results will inform the limits of telepractice for dysarthria evaluation.

项目总结/摘要 随着远程实践越来越受欢迎，确实是必要的，替代诊所访问，演讲- 语言病理学家（SLP）评估语音，该语音是通过远程网络捕获和传输的音频。 电话会议解决方案（例如Zoom）。所有流行的电话会议应用程序都使用语音压缩 基于线性预测编码（LPC）的算法，以减少语音传输所需的带宽。LPC 压缩算法将语音分解为发声源和发音滤波器参数， 然后基于已经开发的方案独立地进行矢量量化和时间平滑 并且被优化用于压缩从一般人群中采样的语音。通过这种方式， 对于给定的因特网连接具有最少数量的可听伪像和最高级别的可理解性 约束由于LPC算法是使用大型典型语音语料库进行优化的， 基本上不适合于忠实地传输通常存在于 构音障碍的语音信号以及发音和语音特征特别容易受到破坏。在这 建议的目的是系统地描述语音压缩算法的影响， 用于远程实践平台的语音清晰度，感知评估和声学测量。这 是通过两个目标完成的： SA 1：评估电话会议语音压缩算法在三个互联网上的效果 带宽水平对构音障碍言语的感知和声学评估的影响 现有的单词和句子的高保真音频记录以及20名发言者的持续发声， 将以三种压缩率对各种构音障碍进行编码，以模拟低、中、高压缩率。 宽带互联网连接。20个SLP将转录样本，以达到可理解性措施， 原始和编码的单词和句子;以及对持续发声的声音质量进行感知评级。 发音和声音的声学措施将被提取。受试者内统计模型将评价 语音任务中带宽条件对感知和声学结果影响。 SA 2：比较远程练习会话（压缩）中记录的构音障碍言语与 通过智能手机应用程序（未压缩）同时记录。 15名患有构音障碍的演讲者将参加模拟远程练习语音评估， SLP。随后，会议录音（压缩）和同时录制的现场样本 （未压缩）将由SLP评分。现场录音也将像在SA 1条件下一样进行压缩。 将从所有样本中提取声学指标。受试者内统计模型将评价样本 不同条件下的差异（未压缩，远程练习期间压缩，以及低-中等-和高- 带宽压缩级别）。结果将告知构音障碍评估的远程练习的限制。