Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis.
Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis.
复制标题
验证肌萎缩性脊髓侧索硬化症中构音障碍严重程度和音节任务的自动介电运动分析方法。
DOI：
10.1044/2021_jslhr-21-00503
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Yunusova,Yana
中科院分区：
文献类型：
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作者：
Tanchip,Chelsea;Guarin,DiegoL;McKinlay,Scotia;Barnett,Carolina;Kalra,Sanjay;Genge,Angela;Korngut,Lawrence;Green,JordanR;Berry,James;Zinman,Lorne;Yadollahi,Azadeh;Abrahao,Agessandro;Yunusova,Yana
目的口腔发声运动(DDK)是一项标准的构音障碍评估任务。为了提取自动和半自动的DDK测量，有许多基于声信号处理的DDK分析算法可用，包括基于幅度的、基于频谱的和混合的。然而，这些算法主要在没有可察觉到到轻度构音障碍的个体中得到验证。这些算法在构音障碍严重程度上的行为在很大程度上是未知的。同样，这些算法也没有针对各种音节类型进行同样的测试。本研究的目的是评估五种常见的DDK算法的性能作为构音障碍严重程度的函数，考虑音节类型。方法我们分析了来自145名肌萎缩侧索硬化症患者的282个/ba/，/pa/和/ta/的DDK记录。根据个人在语音清晰度测试中的表现，将录音分成轻度、中度或重度构音障碍组。分析包括手动和自动估计音节数量、DDK比率和周期间时间变异性(CTV)。验证指标包括手动和自动音节计数之间的Bland-Altman混合效应一致性限度，手动和自动音节边界检测之间的召回率和精确度，以及手动和算法检测的DDK率和cTV之间的肯德尔tau-b相关性。结果基于幅度的算法(绝对能量)在所有严重程度组的DDK率(τb=0.7-0.84)和CTV(τb=0.7-0.84)以及最窄的一致性限度(−5.92至7.12音节差异)中与手动分析的相关性最强。此外，该算法对/ba/和/pa/的平均召回率和准确率也最高，但对/ta/的差异明显更大。结论基于信号能量分析的算法对于构音障碍严重程度和音节类型的DDK分析似乎是最健壮的；但在严重构音障碍和牙槽音节的情况下，它仍然容易出错。需要进一步发展，以解决这一重要问题。
PurposeOral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms have been predominantly validated in individuals with no perceptible to mild dysarthria. The behavior of these algorithms across dysarthria severity is largely unknown. Likewise, these algorithms have not been tested equally for various syllable types. The goal of this study was to evaluate the performance of five common DDK algorithms as a function of dysarthria severity, considering syllable types.MethodWe analyzed 282 DDK recordings of /ba/, /pa/, and /ta/ from 145 participants with amyotrophic lateral sclerosis. Recordings were stratified into mild, moderate, or severe dysarthria groups based on individual performance on the Speech Intelligibility Test. Analysis included manual and automatic estimation of the number of syllables, DDK rate, and cycle-to-cycle temporal variability (cTV). Validation metrics included Bland–Altman mixed-effects limits of agreement between manual and automatic syllable counts, recall and precision between manual and automatic syllable boundary detection, and Kendall's tau-b correlations between manual and algorithm-detected DDK rate and cTV.ResultsThe amplitude-based algorithm (absolute energy) yielded the strongest correlations with manual analysis across all severity groups for DDK rate (τb= 0.7–0.84) and cTV (τb= 0.7–0.84) and the narrowest limits of agreement (−5.92 to 7.12 syllable difference). Moreover, this algorithm also provided the highest mean recall and precision across severity groups for /ba/ and /pa/, but with significantly more variation for/ta/.ConclusionsAlgorithms based on signal energy analysis appeared to be the most robust for DDK analysis across dysarthria severity and syllable types; however, it remains prone to error against severe dysarthria and alveolar syllable context. Further development is needed to address this important issue.