HCC: Small: Collaborative Research: Analysis of Language Samples for Detecting Language Impairment in Monolingual and Bilingual Children

HCC：小型：合作研究：分析语言样本以检测单语和双语儿童的语言障碍

• 批准号: 1462143
• 负责人: Thamar Solorio
• 金额: $ 13.05万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-31 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462143&HistoricalAwards=false
• 关键词: HCC; Small; Collaborative; Research; Analysis

It is widely recognized that language impairment can have a negative effect on literacy skills, and that children suffering language impairment are at a higher risk of academic under-achievement and lower overall social development. Hence, early and accurate language assessment for children is critical, especially for those with non-mainstream linguistic backgrounds. Spontaneous language samples are commonly used in communication disorders to measure the speaker's competence across a range of complementary language skills. These elicitation tasks allow clinicians and clinical researchers to analyze speech fluency by looking at the patterns of disfluencies and other speech disruptions. Language productivity can be gauged by computing mean length of utterance, along with measures of vocabulary and total utterances produced. Morpho-syntactic skills can also be analyzed from these data, by manually coding for specific grammatical constructions that are known to signal developmental milestones. At present, use of the information contained in these language samples is restricted to the capacity of human experts to manually analyze the data, since little has been done to use computational models for this task In this collaborative effort by PIs in the University of Alabama at Birmingham and the University of Texas at Dallas, the objective is to address this problem by developing computational approaches for scoring samples from children along different language dimensions, including speech fluency, syntactic structure, content, and coherence, with the long term goal of building robust computational linguistic approaches for identifying language impairments in children. With these ends in mind, the PIs will investigate a number of core research questions, including measuring syntactic complexity in children's language, evaluating content in story retelling and play sessions, and detecting disfluencies in children's transcripts. Moreover, this research will focus on analyzing samples from children with three different language backgrounds: English monolinguals, Spanish monolinguals, and Spanish-English bilinguals of Mexican descent (the latter representing the fastest growing minority in this country). Since their models will be data driven, the PIs expect to be able to evaluate empirically the differences in developmental patterns of speech in children across these linguistic diversities. Addressing the bilingual population involves modeling code-switching behavior; thus, additional core research questions include measuring syntactic complexity in code-switched data, and identification and categorization of code-switching patterns in bilingual children. Broader Impacts: This research will contribute to developing more accurate and practical tools for assessing language development in children, a field to which little attention has been paid to date. Addressing the challenges involved in the automated analysis of children's speech will also advance the field of Natural Language Processing (NLP) in general. Moreover, since the project involves children with three different linguistic backgrounds, the new technology will have low language dependency and so should be easily portable to other languages and domains. In the field of communication disorders, applying corpus-based approaches to language assessment is still in its infancy; project outcomes will have a direct impact on this field, by providing new metrics for scoring spontaneous language samples of children that can complement the battery of assessment tools currently used.

人们普遍认为，语言障碍会对读写能力产生负面影响，并且患有语言障碍的儿童学业成绩不佳和整体社会发展水平较低的风险更高。 因此，对儿童进行早期、准确的语言评估至关重要，尤其是对于那些具有非主流语言背景的儿童。 自发语言样本通常用于沟通障碍，以衡量说话者在一系列互补语言技能方面的能力。 这些启发任务使临床医生和临床研究人员能够通过查看不流畅和其他言语中断的模式来分析言语流畅性。 语言生产力可以通过计算平均话语长度以及词汇量和总话语量来衡量。 通过手动编码已知可表示发育里程碑的特定语法结构，还可以从这些数据中分析形态句法技能。 目前，这些语言样本中包含的信息的使用仅限于人类专家手动分析数据的能力，因为在使用计算模型来完成这项任务方面几乎没有采取任何措施。伯明翰和德克萨斯大学达拉斯分校的目标是通过开发计算方法来解决这个问题，根据不同的语言维度对儿童样本进行评分，包括言语流畅性、句法结构、内容和连贯性，长期目标是建立强大的语言能力用于识别语言的计算语言学方法儿童的障碍。 考虑到这些目标，PI 将调查一些核心研究问题，包括测量儿童语言的句法复杂性、评估故事复述和游戏环节的内容，以及检测儿童笔录中的不流畅之处。 此外，这项研究将重点分析来自三种不同语言背景的儿童样本：英语单语者、西班牙语单语者和墨西哥裔西班牙语-英语双语者（后者代表了该国增长最快的少数族裔）。 由于他们的模型将是数据驱动的，PI 希望能够凭经验评估不同语言多样性的儿童言语发展模式的差异。 解决双语人群问题涉及对语码转换行为进行建模；因此，其他核心研究问题包括测量语码转换数据中的句法复杂性，以及双语儿童语码转换模式的识别和分类。 更广泛的影响：这项研究将有助于开发更准确、更实用的工具来评估儿童语言发展，而迄今为止，这一领域很少受到关注。 解决儿童语音自动分析所涉及的挑战也将推动自然语言处理 (NLP) 领域的发展。 此外，由于该项目涉及具有三种不同语言背景的儿童，因此新技术的语言依赖性较低，因此应该可以轻松移植到其他语言和领域。 在沟通障碍领域，应用基于语料库的语言评估方法仍处于起步阶段；项目成果将通过提供对儿童自发语言样本进行评分的新指标来对该领域产生直接影响，这些指标可以补充目前使用的一系列评估工具。