McKechnie J, Ahmed B, Gutierrez-Osuna R, Monroe P, McCabe P, Ballard K J
a Faculty of Health Sciences , University of Sydney , Lidcombe , NSW , Australia.
b Department of Electrical and Computer Engineering , Texas A&M University , Doha , Qatar , and.
Int J Speech Lang Pathol. 2018 Nov;20(6):583-598. doi: 10.1080/17549507.2018.1477991. Epub 2018 Jul 11.
A systematic search and review of published studies was conducted on the use of automated speech analysis (ASA) tools for analysing and modifying speech of typically-developing children learning a foreign language and children with speech sound disorders to determine (i) types, attributes, and purposes of ASA tools being used; (ii) accuracy against human judgment; and (iii) performance as therapeutic tools.
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Across nine databases, 32 articles published between January 2007 and December 2016 met inclusion criteria: (i) focussed on children's speech; (ii) tools used for speech analysis or modification; and (iii) reporting quantitative data on accuracy.
Eighteen ASA tools were identified. These met the clinical threshold of 80% agreement with human judgment when used as predictors of intelligibility, impairment severity, or error category. Tool accuracy was typically <80% accuracy for words containing mispronunciations. ASA tools have been used effectively to improve to children's foreign language pronunciation.
ASA tools show promise for automated analysis and modification of children's speech production within assessment and therapeutic applications. Further work is needed to train automated systems with larger samples of speech to increase accuracy for assessment and therapeutic feedback.
对已发表的研究进行系统检索和综述,这些研究涉及使用自动语音分析(ASA)工具来分析和修改学习外语的发育正常儿童以及有语音障碍儿童的语音,以确定(i)所使用的ASA工具的类型、属性和用途;(ii)与人类判断相比的准确性;以及(iii)作为治疗工具的性能。
应用系统评价和Meta分析的首选报告项目(PRISMA)指南。在九个数据库中,2007年1月至2016年12月发表的32篇文章符合纳入标准:(i)专注于儿童语音;(ii)用于语音分析或修改的工具;以及(iii)报告关于准确性的定量数据。
识别出18种ASA工具。当用作可懂度、损伤严重程度或错误类别的预测指标时,这些工具与人类判断的一致性达到了80%的临床阈值。对于包含发音错误的单词,工具的准确性通常低于80%。ASA工具已被有效地用于改善儿童的外语发音。
ASA工具在评估和治疗应用中对儿童语音产生的自动分析和修改显示出前景。需要进一步开展工作,用更大的语音样本训练自动化系统,以提高评估和治疗反馈的准确性。