Purpose Spoken language sample analysis (LSA) is widely considered to be a critical component of assessment for child language disorders. It is our best window into a preschool child's everyday expressive communicative skills. However, historically, the process can be cumbersome, and reference values against which LSA findings can be "benchmarked" are based on surprisingly little data. Moreover, current LSA protocols potentially disadvantage speakers of nonmainstream English varieties, such as African American English (AAE), blurring the line between language difference and disorder. Method We provide a tutorial on the use of free software (Computerized Language Analysis [CLAN]) enabled by the ongoing National Institute on Deafness and Other Communication Disorders-funded "Child Language Assessment Project." CLAN harnesses the advanced computational power of the Child Language Data Exchange System archive (www.childes.talkbank.org), with an aim to develop and test fine-grained and potentially language variety-sensitive benchmarks for a range of LSA measures. Using retrospective analysis of data from AAE-speaking children, we demonstrate how CLAN LSA can facilitate dialect-fair assessment and therapy goal setting. Results Using data originally collected to norm the Diagnostic Evaluation of Language Variation, we suggest that Developmental Sentence Scoring does not appear to bias against children who speak AAE but does identify children who have language impairment (LI). Other LSA measure scores were depressed in the group of AAE-speaking children with LI but did not consistently differentiate individual children as LI. Furthermore, CLAN software permits rapid, in-depth analysis using Developmental Sentence Scoring and the Index of Productive Syntax that can identify potential intervention targets for children with developmental language disorder.

中文翻译：

---

使用免费的计算机辅助语言样本分析来评估和设定讲非裔美国人英语的儿童的治疗目标。

目的 口语样本分析 (LSA) 被广泛认为是评估儿童语言障碍的重要组成部分。这是我们了解学龄前儿童日常表达沟通技巧的最佳窗口。然而，从历史上看，这个过程可能很繁琐，并且 LSA 发现可以“基准化”的参考值是基于令人惊讶的少量数据。此外，当前的 LSA 协议可能会使非主流英语变体的使用者处于不利地位，例如非裔美国人英语 (AAE)，从而模糊了语言差异和无序之间的界限。方法 我们提供免费软件（计算机语言分析 [CLAN]）的使用教程，该软件由正在进行的国家耳聋和其他交流障碍研究所资助的“儿童语言评估项目”启用。CLAN 利用儿童语言数据交换系统档案 (www.childes.talkbank.org) 的先进计算能力，旨在为一系列 LSA 测量开发和测试细粒度且可能对语言多样性敏感的基准。通过对讲 AAE 的儿童的数据进行回顾性分析，我们展示了 CLAN LSA 如何促进方言公平评估和治疗目标设定。结果 使用最初收集的用于规范语言变异诊断评估的数据，我们建议发育句子评分似乎不会偏向讲 AAE 的儿童，但确实可以识别有语言障碍 (LI) 的儿童。其他 LSA 测量分数在讲 AAE 的 LI 儿童组中被抑制，但并没有始终如一地将个别儿童区分为 LI。此外，