Review of methods for conducting speech research with minimally verbal individuals with autism spectrum disorder.

IF 2.1 3区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY Augmentative and Alternative Communication Pub Date : 2023-03-01 Epub Date: 2022-11-08 DOI:10.1080/07434618.2022.2120071
Karen V Chenausky, Marc Maffei, Helen Tager-Flusberg, Jordan R Green
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Abstract

The purpose of this paper was to review best-practice methods of collecting and analyzing speech production data from minimally verbal autistic speakers. Data on speech production data in minimally verbal individuals are valuable for a variety of purposes, including phenotyping, clinical assessment, and treatment monitoring. Both perceptual ("by ear") and acoustic analyses of speech can reveal subtle improvements as a result of therapy that may not be apparent when correct/incorrect judgments are used. Key considerations for collecting and analyzing speech production data from this population are reviewed. The definition of "minimally verbal" that is chosen will vary depending on the specific hypotheses investigated, as will the stimuli to be collected and the task(s) used to elicit them. Perceptual judgments are ecologically valid but subject to known sources of bias; therefore, training and reliability procedures for perceptual analyses are addressed, including guidelines on how to select vocalizations for inclusion or exclusion. Factors to consider when recording and acoustically analyzing speech are also briefly discussed. In summary, the tasks, stimuli, training methods, analysis type(s), and level of detail that yield the most reliable data to answer the question should be selected. It is possible to obtain rich high-quality data even from speakers with very little speech output. This information is useful not only for research but also for clinical decision-making and progress monitoring.

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回顾对自闭症谱系障碍患者进行言语研究的方法。
本文旨在评述收集和分析极少言语自闭症患者语音生成数据的最佳实践方法。极少言语者的言语生成数据对表型分析、临床评估和治疗监测等多种用途都很有价值。对言语的感知("耳朵")和声学分析都能揭示治疗带来的微妙改善,而这些改善在使用正确/错误判断时可能并不明显。本文回顾了收集和分析这类人群语音生成数据的主要注意事项。根据研究的具体假设,所选择的 "最小言语 "的定义会有所不同,所收集的刺激和用于激发刺激的任务也会有所不同。知觉判断在生态学上是有效的,但也存在已知的偏差来源;因此,我们将讨论知觉分析的培训和可靠性程序,包括如何选择发声进行包含或排除的指导原则。此外,还简要讨论了在对语音进行录音和声学分析时需要考虑的因素。总之,应选择能产生最可靠数据以回答问题的任务、刺激物、训练方法、分析类型和详细程度。即使说话人的语音输出很少,也有可能获得丰富的高质量数据。这些信息不仅对研究有用,而且对临床决策和进展监测也很有用。
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来源期刊
Augmentative and Alternative Communication
Augmentative and Alternative Communication AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY-
CiteScore
2.80
自引率
15.00%
发文量
25
审稿时长
>12 weeks
期刊介绍: As the official journal of the International Society for Augmentative and Alternative Communication (ISAAC), Augmentative and Alternative Communication (AAC) publishes scientific articles related to the field of augmentative and alternative communication (AAC) that report research concerning assessment, treatment, rehabilitation, and education of people who use or have the potential to use AAC systems; or that discuss theory, technology, and systems development relevant to AAC. The broad range of topic included in the Journal reflects the development of this field internationally. Manuscripts submitted to AAC should fall within one of the following categories, AND MUST COMPLY with associated page maximums listed on page 3 of the Manuscript Preparation Guide. Research articles (full peer review), These manuscripts report the results of original empirical research, including studies using qualitative and quantitative methodologies, with both group and single-case experimental research designs (e.g, Binger et al., 2008; Petroi et al., 2014). Technical, research, and intervention notes (full peer review): These are brief manuscripts that address methodological, statistical, technical, or clinical issues or innovations that are of relevance to the AAC community and are designed to bring the research community’s attention to areas that have been minimally or poorly researched in the past (e.g., research note: Thunberg et al., 2016; intervention notes: Laubscher et al., 2019).
期刊最新文献
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