Gonzalez-Moreira Eduardo, Torres-Boza Diana, Kairuz Héctor Arturo, Ferrer Carlos, Garcia-Zamora Marlene, Espinoza-Cuadros Fernando, Hernandez-Gómez Luis Alfonso
Center for Studies on Electronics and Information Technologies, Universidad Central "Marta Abreu" de Las Villas, 54830 Santa Clara, Cuba.
Center for Elderly Adults No. 2, 54830 Santa Clara, Cuba.
Biomed Res Int. 2015;2015:916356. doi: 10.1155/2015/916356. Epub 2015 Oct 19.
This paper describes an exploratory technique to identify mild dementia by assessing the degree of speech deficits. A total of twenty participants were used for this experiment, ten patients with a diagnosis of mild dementia and ten participants like healthy control. The audio session for each subject was recorded following a methodology developed for the present study. Prosodic features in patients with mild dementia and healthy elderly controls were measured using automatic prosodic analysis on a reading task. A novel method was carried out to gather twelve prosodic features over speech samples. The best classification rate achieved was of 85% accuracy using four prosodic features. The results attained show that the proposed computational speech analysis offers a viable alternative for automatic identification of dementia features in elderly adults.
本文描述了一种通过评估言语缺陷程度来识别轻度痴呆的探索性技术。本实验共使用了20名参与者，其中10名被诊断为轻度痴呆的患者和10名作为健康对照的参与者。按照为本研究开发的方法，对每个受试者的音频进行了录制。通过对阅读任务进行自动韵律分析，测量了轻度痴呆患者和健康老年对照者的韵律特征。采用一种新方法从语音样本中提取12个韵律特征。使用四个韵律特征实现的最佳分类率为85%。所得结果表明，所提出的计算语音分析为自动识别老年人痴呆特征提供了一种可行的替代方法。
