Karlsson Fredrik, Schalling Ellika, Laakso Katja, Johansson Kerstin, Hartelius Lena
Department of Clinical Science, Speech and Language Pathology, Umeå University, Umeå SE90187, Sweden.
Department of Clinical Science, Intervention and Technology, Division of Speech and Language Pathology, Karolinska Institutet, Stockholm SE14186, Sweden.
J Acoust Soc Am. 2020 Feb;147(2):839. doi: 10.1121/10.0000581.
This investigation aimed at determining whether an acoustic quantification of the oral diadochokinetic (DDK) task may be used to predict the perceived level of speech impairment when speakers with Parkinson's disease (PD) are reading a standard passage. DDK sequences with repeated [pa], [ta], and [ka] syllables were collected from 108 recordings (68 unique speakers with PD), along with recordings of the speakers reading a standardized text. The passage readings were assessed in five dimensions individually by four speech-language pathologists in a blinded and randomized procedure. The 46 acoustic DDK measures were merged with the perceptual ratings of read speech in the same recording session. Ordinal regression models were trained repeatedly on 80% of ratings and acoustic DDK predictors per dimension in 10-folds, and evaluated in testing data. The models developed from [ka] sequences achieved the best performance overall in predicting the clinicians' ratings of passage readings. The developed [pa] and [ta] models showed a much lower performance across all dimensions. The addition of samples with severe impairments and further automation of the procedure is required for the models to be used for screening purposes by non-expert clinical staff.
本研究旨在确定当帕金森病（PD）患者朗读标准段落时，口腔轮替运动速率（DDK）任务的声学量化是否可用于预测其言语障碍的感知程度。从108份录音（68名患有PD的独特患者）中收集了重复发[pa]、[ta]和[ka]音节的DDK序列，以及患者朗读标准化文本的录音。由四名言语病理学家以盲法和随机程序分别从五个维度对段落朗读进行评估。在同一录音环节中，将46项声学DDK测量结果与朗读语音的感知评分相结合。在10折交叉验证中，序数回归模型在每个维度上对80%的评分和声学DDK预测因子进行反复训练，并在测试数据中进行评估。从[ka]序列开发的模型在预测临床医生对段落朗读的评分方面总体表现最佳。所开发的[pa]和[ta]模型在所有维度上的表现要低得多。为使这些模型可供非专业临床人员用于筛查目的，需要增加严重受损样本并进一步实现程序自动化。
