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

人们普遍认识到，语言障碍会对识字技能产生负面影响，语言障碍儿童学习成绩不佳的风险更高，整体社会发展水平更低。因此，对儿童进行早期和准确的语言评估是至关重要的，特别是对那些具有非主流语言背景的儿童。自发的语言样本通常用于交流障碍，以衡量说话人在一系列互补语言技能上的能力。这些启发式任务允许临床医生和临床研究人员通过观察不流利和其他语言中断的模式来分析语言流畅性。语言生产力可以通过计算话语的平均长度以及词汇量和产生的总话语来衡量。形态句法技能也可以从这些数据中分析出来，通过手动编码特定的语法结构，这些结构被认为是发展里程碑的信号。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，因为在阿拉巴马大学伯明翰分校和德克萨斯大学达拉斯分校的PI的合作努力中，几乎没有为这项任务使用计算模型，目标是通过开发计算方法来对不同语言维度的儿童样本进行评分，包括语音流利性、句法结构、内容和连贯性，长期目标是建立稳健的计算语言方法来识别儿童的语言障碍。考虑到这些目的，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，以及检测儿童成绩单中的不流利。此外，这项研究将重点分析来自三种不同语言背景的儿童的样本：英语单一语言者、西班牙语单一语言者和墨西哥后裔的西班牙语-英语双语者(后者代表着这个国家增长最快的少数民族)。由于他们的模型将是数据驱动的，PI希望能够对这些语言多样性上的儿童言语发展模式的差异进行经验性评估。解决双语人群的问题涉及对语码转换行为的建模；因此，其他核心研究问题包括测量语码转换数据的句法复杂性，以及识别和分类双语儿童的语码转换模式。更广泛的影响：这项研究将有助于开发更准确和实用的工具来评估儿童的语言发展，这是一个迄今鲜有人关注的领域。解决儿童语音自动分析所涉及的挑战也将推动整个自然语言处理(NLP)领域的发展。此外，由于该项目涉及三种不同语言背景的儿童，新技术将具有较低的语言依赖性，因此应该很容易移植到其他语言和领域。在沟通障碍领域，将基于语料库的方法应用于语言评估仍处于初级阶段；项目成果将通过为儿童自发语言样本评分提供新的衡量标准，从而补充目前使用的评估工具，从而对这一领域产生直接影响。

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.