Gorman Kyle, Bedrick Steven, Kiss Géza, Morley Eric, Ingham Rosemary, Mohammad Metrah, Papadakis Katina, van Santen Jan P H
Center for Spoken Language Understanding, Oregon Health & Science University, Portland, OR, USA.
Proc Conf. 2015 Jun 5;2015:108-116.
Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.
临床语言样本的定量分析是评估和筛查发育性语言障碍的有力工具，但需要大量的人工转录、注释和计算，结果容易出错且在临床上未得到充分利用。我们描述了一种系统，该系统执行计算诸如词素平均语句长度（MLUM）等统计数据所需的自动形态分析，以便这些统计数据可以直接从正字法转录本中计算得出。该系统计算的MLUM估计值与人工注释产生的估计值非常可比。我们的系统可以与其他自动注释技术（如迷宫检测）结合使用。这项工作代表了朝着提高语言样本分析自动化程度以及实现自动化带来的相关好处（包括在临床上更多地利用和减少护理提供中的变异性）迈出的重要第一步。
