Automatic Voice-Based Assessment of Language Abilities

 描述(申请人提供)：由于未经治疗的语言障碍--一种患病率至少为7%的障碍--可能会导致严重的行为和教育问题，因此迫切需要大规模的早期语言评估，不仅是为了及早识别语言障碍，也是为了规划干预措施和跟踪进展。这是因为最近的一项研究发现，71%被诊断为特定语言障碍(一种语言障碍)的儿童以前没有被识别出来。然而，这种大规模的努力将给专业人员和其他稀缺资源带来巨大负担。因此，临床医生、教育工作者和研究人员一直主张使用基于计算机的评估。最近，基于计算机的语言评估取得了进展，但它仅限于语言理解(即接受性词汇和语法)。因此，仍然缺乏基于计算机的语言产出的评估，即表达语言，特别是话语技能。一个促成因素是 这方面所需的关键技术自动语音识别(ASR)被认为不足以对语言测试进行准确评分，因为即使是最好的ASR系统也有超过20%的单词错误率。然而，这种看法是基于对如何使用ASR进行评估的有限视角，在这种情况下，通用ASR系统提供儿童语音的抄本(通常不准确)，然后根据传统规则自动评分。我们采取了另一种观点，并提出了一种创新的方法，包括两个核心概念。首先是创建专用的、针对测试的ASR系统，其搜索空间与测试可能引发的响应空间仔细匹配。第二种是将这些系统与基于机器学习的评分算法相结合，其中，后者不对由ASR系统生成的最终的“最佳”成绩单进行操作，而是对ASR系统在识别输入语音的过程中计算的中间表示的丰富层(“丰富表示”)进行操作。在我们实验室的早期实验中，数字和叙述性回忆测试已经证明了这种方法的可行性。在拟议的项目中，我们将为表达情态以及词汇、语法和语篇领域的测试创建基于计算机的评分和考试管理系统；我们还将为非单词重复测试创建一个系统。这些系统将应用于300名年龄在3-9岁之间、有典型发育和神经发育障碍的儿童，并将根据传统的语言测量标准进行验证。该项目开发的自动化语言测试涵盖了语言障碍的核心诊断标准，但也为更广泛的基于语音的语言和认知评估测试的计算机化奠定了技术基础。

 DESCRIPTION (provided by applicant): Since untreated language disorder - a disorder with a prevalence of at least 7% - can lead to serious behavioral and educational problems, large-scale early language assessment is urgently needed not only for early identification of language disorder but also for planning interventions and tracking progress. This is all the more so because a recent study found that 71% of children diagnosed with Specific Language Impairment (a type of language disorder) had not been previously identified. However, such large-scale efforts would pose a large burden on professional staff and on other scarce resources. As a result, clinicians, educators, and researchers have argued for the use of computer based assessment. Recently, progress has been made with computer based language assessment, but it has been limited to language comprehension (i.e., receptive vocabulary and grammar). Thus, computer based assessment of language production that is expressive language and particularly discourse skills, is still lacking. One contributing factor is that a key technology needed for this, Automatic Speech Recognition (ASR), is perceived as inadequate for accurate scoring of language tests since even the best ASR systems have word error rates in excess of 20%. However, this perception is based on a limited perspective of how ASR can be used for assessment, in which a general- purpose ASR system provides an (often inaccurate) transcript of the child's speech, which then would be scored automatically according to conventional rules. We take an alternative perspective, and propose an innovative approach that comprises two core concepts. The first is that of creating special-purpose, test-specific ASR systems whose search space is carefully matched to the space of responses a test may elicit. The second is that of integrating these systems with machine-learning based scoring algorithms whereby the latter operate not on the final, "best" transcript generated by the ASR system but on the rich layers of intermediate representations that the ASR system computes in the process of recognizing the input speech ("rich representation"). Earlier experiments in our lab with digit and narrative recall tests have demonstrated the feasibility of this approach. In the proposed project we will create computer-based scoring and test administration systems for tests in the expressive modality as well as in the vocabulary, grammar, and discourse domains; we will also create a system for a non-word repetition test. The systems will be applied to a diverse group of 300 children ages 3-9 with typical development and with neurodevelopmental disorders, and will be validated against conventional language measures. The automated language tests developed in the project cover core diagnostic criteria for language disorders but also create a technological foundation for the computerization of a much broader array of tests for voice based language and cognitive assessment.