Abstract：
UNASSIGNED: This study examines the accuracy of Interaction Detection in Early Childhood Settings (IDEAS), a program that automatically transcribes audio files and estimates linguistic units relevant to speech-language therapy, including part-of-speech units that represent features of language complexity, such as adjectives and coordinating conjunctions.
UNASSIGNED: Forty-five video-recorded speech-language therapy sessions involving 27 speech-language pathologists (SLPs) and 56 children were used. The F measure determines the accuracy of IDEAS diarization (i.e., speech segmentation and speaker classification). Two additional evaluation metrics, namely, median absolute relative error and correlation, indicate the accuracy of IDEAS for the estimation of linguistic units as compared with two conditions, namely, Oracle (manual diarization) and Voice Type Classifier (existing diarizer with acceptable accuracy).
UNASSIGNED: The high F measure for SLP talk data suggests high accuracy of IDEAS diarization for SLP talk but less so for child talk. These differences are reflected in the accuracy of IDEAS linguistic unit estimates. IDEAS median absolute relative error and correlation values for nine of the 10 SLP linguistic unit estimates meet the accuracy criteria, but none of the child linguistic unit estimates meet these criteria. The type of linguistic units also affects IDEAS accuracy.
UNASSIGNED: IDEAS was tailored to educational settings to automatically convert audio recordings into text and to provide linguistic unit estimates in speech-language therapy sessions and classroom settings. Although not perfect, IDEAS is reliable in automatically capturing and returning linguistic units, especially in SLP talk, that are relevant in research and practice. The tool offers a way to automatically measure SLP talk in clinical settings, which will support research seeking to understand how SLP talk influences children\'s language growth.
UNASSIGNED: Forty-five video-recorded speech-language therapy sessions involving 27 speech-language pathologists (SLPs) and 56 children were used. The F measure determines the accuracy of IDEAS diarization (i.e., speech segmentation and speaker classification). Two additional evaluation metrics, namely, median absolute relative error and correlation, indicate the accuracy of IDEAS for the estimation of linguistic units as compared with two conditions, namely, Oracle (manual diarization) and Voice Type Classifier (existing diarizer with acceptable accuracy).
UNASSIGNED: The high F measure for SLP talk data suggests high accuracy of IDEAS diarization for SLP talk but less so for child talk. These differences are reflected in the accuracy of IDEAS linguistic unit estimates. IDEAS median absolute relative error and correlation values for nine of the 10 SLP linguistic unit estimates meet the accuracy criteria, but none of the child linguistic unit estimates meet these criteria. The type of linguistic units also affects IDEAS accuracy.
UNASSIGNED: IDEAS was tailored to educational settings to automatically convert audio recordings into text and to provide linguistic unit estimates in speech-language therapy sessions and classroom settings. Although not perfect, IDEAS is reliable in automatically capturing and returning linguistic units, especially in SLP talk, that are relevant in research and practice. The tool offers a way to automatically measure SLP talk in clinical settings, which will support research seeking to understand how SLP talk influences children\'s language growth.
摘要：
■这项研究检查了早期儿童环境中的交互检测（IDEAS）的准确性，自动转录音频文件并估计与语言治疗相关的语言单位的程序，包括代表语言复杂性特征的词性单元，如形容词和协调连词。
■使用了45次视频录制的言语语言治疗课程，涉及27名言语语言病理学家（SLP）和56名儿童。F度量确定IDEAS二值化的准确性(即，语音分割和说话人分类)。另外两个评估指标，即,中位数绝对相对误差和相关性，与两个条件相比，表明IDEAS对语言单位估计的准确性，即,Oracle（手动二值化）和语音类型分类器（现有的具有可接受精度的二值化器）。
■SLP谈话数据的高F度量表明，SLP谈话的IDEASdiarization具有很高的准确性，但儿童谈话的准确性较低。这些差异反映在IDEAS语言单位估计的准确性中。IDEAS10个SLP语言单元估计值中有9个的中值绝对相对误差和相关值符合准确性标准，但是儿童语言单位估计都不符合这些标准。语言单位的类型也会影响IDEAS的准确性。
■IDEAS是针对教育环境量身定制的，可以自动将录音转换为文本，并在言语语言治疗课程和课堂环境中提供语言单位估计。虽然不完美，IDEAS在自动捕获和返回语言单位方面是可靠的，尤其是在SLP演讲中，这与研究和实践有关。该工具提供了一种在临床环境中自动测量SLP通话的方法，这将支持旨在了解SLP谈话如何影响儿童语言成长的研究。
■使用了45次视频录制的言语语言治疗课程，涉及27名言语语言病理学家（SLP）和56名儿童。F度量确定IDEAS二值化的准确性(即，语音分割和说话人分类)。另外两个评估指标，即,中位数绝对相对误差和相关性，与两个条件相比，表明IDEAS对语言单位估计的准确性，即,Oracle（手动二值化）和语音类型分类器（现有的具有可接受精度的二值化器）。
■SLP谈话数据的高F度量表明，SLP谈话的IDEASdiarization具有很高的准确性，但儿童谈话的准确性较低。这些差异反映在IDEAS语言单位估计的准确性中。IDEAS10个SLP语言单元估计值中有9个的中值绝对相对误差和相关值符合准确性标准，但是儿童语言单位估计都不符合这些标准。语言单位的类型也会影响IDEAS的准确性。
■IDEAS是针对教育环境量身定制的，可以自动将录音转换为文本，并在言语语言治疗课程和课堂环境中提供语言单位估计。虽然不完美，IDEAS在自动捕获和返回语言单位方面是可靠的，尤其是在SLP演讲中，这与研究和实践有关。该工具提供了一种在临床环境中自动测量SLP通话的方法，这将支持旨在了解SLP谈话如何影响儿童语言成长的研究。
