Gale Robert, Dolata Jill, Prud'hommeaux Emily, van Santen Jan, Asgari Meysam
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:6111-6114. doi: 10.1109/EMBC44109.2020.9175264.
This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance-We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.
本研究描述了一种基于儿童对句子重复任务（SRT）的语音反应的全自动表达性语言评估方法，SRT是一种能够挖掘核心语言技能的语言测试。我们提出的方法使用特定于测试的自动语音识别系统自动转录语音反应。根据转录内容，回归模型预测言语病理学家提供的金标准测试分数。我们对104名儿童（43名发育正常，61名患有神经发育障碍）的录音进行的初步实验结果验证了所提出的自动方法在该语言测试中预测金标准分数的可行性，观察评分与预测评分之间的平均绝对误差为6.52（观察分数范围为0至90，平均值为49.56）。临床相关性——我们描述了在语言评估中使用基于语音的全自动评分，包括这一进展可能对言语病理学领域产生的临床影响。这种自动化测试还为基于语音的语言评估的一系列测试的计算机化奠定了技术基础。
