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

基本信息

批准号：
10383726
负责人：
Visar Berisha
金额：
$ 18.34万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-05-31
项目状态：
已结题

项目摘要

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.

项目概要/摘要随着远程治疗变得越来越流行，而且确实是必要的，可以替代诊所就诊，语音- 语言病理学家 (SLP) 评估通过音频捕获并远程传输的语音电话会议解决方案（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）的算法可减少语音传输所需的带宽。线性PC 压缩算法将语音分解为发声源和发音滤波器参数，它们是然后根据已开发的方案进行独立矢量量化和时间平滑并针对压缩从一般人群中采样的语音进行了优化。这样就可以传输语音了对于给定的互联网连接，声音干扰最少，清晰度最高限制。由于 LPC 算法是使用大型典型语音语料库进行优化的，因此它们从根本上来说不太适合忠实地传输通常存在的失真或噪声量构音障碍的语音信号、发音和语音特征特别容易受到损坏。在这个提案的目的是系统地描述语音压缩算法的影响用于语音清晰度、感知评估和声学测量的远程练习平台。这是通过两个目标来实现的： SA1：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语音感知和声学评估的影响现有的单词和句子的高保真录音以及 20 个扬声器的持续发音各种构音障碍将以三种压缩率进行编码，以模拟低、中、高带宽互联网连接。二十个 SLP 将转录样本以获得该内容的可懂度测量原始和编码的单词和句子；并通过感知评估持续发声的声音质量。将提取发音和声音的声学测量值。受试者内统计模型将评估带宽条件对语音任务中感知和声学结果的影响。 SA2：比较远程练习会话中记录的构音障碍语音（压缩）与通过智能手机应用程序同时亲自录制（未压缩）。十五名患有构音障碍的演讲者将参加由以下机构管理的模拟远程练习演讲评估： SLP。随后，会话录音（压缩）和同时录制的现场样本（未压缩）将由 SLP 评分。现场录音也将按照 SA1 条件进行压缩。将从所有样本中提取声学指标。受试者内统计模型将评估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-和高- 带宽压缩级别）。结果将告知远程治疗评估构音障碍的局限性。