Department of Communicative Disorders and Deaf Education, Utah State University, Logan, Utah 84322, USA.
Department of Psychology, Utah State University, Logan, Utah 84322, USA.
J Acoust Soc Am. 2019 Jan;145(1):392. doi: 10.1121/1.5087276.
Speech perception studies typically rely on trained research assistants to score orthographic listener transcripts for words correctly identified. While the accuracy of the human scoring protocol has been validated with strong intra- and inter-rater reliability, the process of hand-scoring the transcripts is time-consuming and resource intensive. Here, an open-source computer-based tool for automated scoring of listener transcripts is built (Autoscore) and validated on three different human-scored data sets. Results show that not only is Autoscore highly accurate, achieving approximately 99% accuracy, but extremely efficient. Thus, Autoscore affords a practical research tool, with clinical application, for scoring listener intelligibility of speech.
言语感知研究通常依赖于经过训练的研究助理来为正确识别的单词对音标听众抄本进行评分。虽然人类评分协议的准确性已经通过强的内部和内部评分者可靠性得到验证，但手动评分抄本的过程既费时又费力。在这里，构建了一个用于自动评分听众抄本的开源计算机工具（Autoscore），并在三个不同的人工评分数据集上进行了验证。结果表明，Autoscore 不仅非常准确，准确率约为 99%，而且效率极高。因此，Autoscore 为言语可理解度的听众评分提供了一种实用的研究工具，具有临床应用价值。
