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Transforming assessment of speech in children with cleft palate via online crowdsourcing.

通过在线众包对腭裂患儿语音进行转化评估。

  • 影响因子:3.02
  • DOI:10.1371/journal.pone.0227686
  • 作者列表:"Sescleifer AM","Francoisse CA","Webber JC","Rector JD","Lin AY
  • 发表时间:2020-01-09
Abstract

OBJECTIVE:Speech intelligibility is fundamental to social interactions and a critical surgical outcome in patients with cleft palate. Online crowdsourcing is a burgeoning technology, with potential to mitigate the burden of limited accessibility to speech-language-pathologists (SLPs). This pilot study investigates the concordance of online crowdsourced evaluations of hypernasality with SLP ratings of children with cleft palate. METHODS:Six audio-phrases each from children with cleft palate were assessed by online crowdsourcing using Amazon Mechanical Turk (MTurk), and compared to SLP's gold-standard hypernasality score on the Pittsburgh Weighted Speech Score (PWSS). Phrases were presented to MTurk crowdsourced lay-raters to assess hypernasality on a Likert scale analogous to the PWSS. The survey included clickable reference audio samples for different levels of hypernasality. RESULTS:1,088 unique online crowdsourced speech ratings were collected on 16 sentences of 3 children with cleft palate aged 4-8 years, with audio averaging 6.5 years follow-up after cleft palate surgery. Patient 1 crowd-mean was 2.62 (SLP rated 2-3); Patient 2 crowd-mean 2.66 (SLP rated 3); and Patient 3 crowd-mean 1.76 (SLP rated 2). Rounded for consistency with PWSS scale, all patients matched SLP ratings. Different sentences had different accuracies compared to the SLP gold standard scores. CONCLUSION:Online crowdsourced ratings of hypernasal speech in children with cleft palate were concordant with SLP ratings, predicting SLP scores in all 3 patients. This novel technology has potential for translation in clinical speech assessments, and may serve as a valuable screening tool for non-experts to identify children requiring further assessment and intervention by a qualified speech language pathology expert.

摘要

目的: 语音清晰度是腭裂患者社会交往和关键手术疗效的基础。在线众包是一种新兴的技术,有可能减轻言语-语言-病理学家 (SLPs) 的有限可及性的负担。本初步研究调查了腭裂儿童过度鼻音的在线众包评价与SLP评级的一致性。 方法: 使用Amazon Mechanical Turk (MTurk) 通过在线众包评估腭裂儿童的 6 个音频短语,并与SLP在匹兹堡加权言语评分 (PWSS) 上的金标准多鼻音评分进行比较。短语被呈现给MTurk众包的外行评定者,以类似于PWSS的Likert量表评估多鼻症。该调查包括不同水平的高鼻音的可点击参考音频样本。 结果: 对 3 例 4-8 岁腭裂患儿的 16 个句子收集了 1,088 个独特的在线众包语音评级,腭裂术后音频平均随访 6.5 年。患者 1 人群-均值为 2.62 (SLP分级 2-3); 患者 2 人群-均值 2.66 (SLP分级 3); 患者 3 人群-均值 1.76 (SLP分级 2)。四舍五入与PWSS量表的一致性,所有患者均匹配SLP评级。与SLP金标准评分相比,不同的句子具有不同的准确性。 结论: 腭裂患儿超鼻语音的在线众包评级与SLP评级一致,预测了所有 3 例患者的SLP评分。这种新技术在临床言语评估中具有翻译的潜力,可以作为非专家识别需要由合格的言语语言病理学专家进一步评估和干预的儿童的有价值的筛查工具。

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影响因子:0.95
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影响因子:2.43
发表时间:2020-01-15
DOI:10.5435/JAAOS-D-16-00918
作者列表:["Grigoryan G","Korcek L","Eidelman M","Paley D","Nelson S"]

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