Machine learning model for discrimination of mild dementia patients using acoustic features
Machine learning model for discrimination of mild dementia patients using acoustic features
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使用声学特征区分轻度痴呆患者的机器学习模型
DOI：
10.1016/j.cogr.2021.12.003
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Nakatoh Yoshihisa
中科院分区：
文献类型：
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作者：
Nishikawa Kazu;Akihiro Kuwahara;Hirakawa Rin;Kawano Hideaki;Nakatoh Yoshihisa
在以往的语音识别痴呆症的研究中，已经提出了一种利用多种声学特征的机器学习方法。然而，他们并没有关注轻度痴呆症患者(MCI)的语音分析。因此，我们提出了一种基于元音发音特征分析的痴呆症识别系统。分析结果表明，在轻度痴呆患者的声音中出现了一些痴呆病例。这些结果也可以作为痴呆症患者未来语音改善的指标。利用这些结果，我们提出了一种基于统计声学特征的分类器和变压器模型神经网络的集成识别系统，F-Score为0.907，优于目前最先进的方法。
In previous research on dementia discrimination by voice, a method using multiple acoustic features by machine learning has been proposed. However, they do not focus on speech analysis in mild dementia patients (MCI). Therefore, we propose a dementia discrimination system based on the analysis of vowel utterance features. The analysis results indicated that some cases of dementia appeared in the voice of mild dementia patients. These results can also be used as an index for future improvement of speech sounds in dementia. Taking advantage of these results, we propose an ensemble discrimination system using a classifier with statistical acoustic features and a Neural Network of transformer models, and the F-score is 0.907, which is better than the state-of-the-art methods.