Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and delay progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of such specialist data leads to uncertainty in both model selection and feature learning when developing such systems. To this end, this paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders on limited data, before the resulting embedding features being fed into an ensemble of backend classifiers to produce the final AD detection decision via majority voting. Experiments conducted on the ADReSS20 Challenge dataset suggest consistent performance improvements were obtained using model and feature combination in system development. State-of-the-art AD detection accuracies of 91.67 percent and 93.75 percent were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers.
翻译:早期诊断阿尔茨海默氏病(AD)对于促进预防性护理和延迟进展至关重要。基于语言的自动自动自动筛选系统为其他临床筛查技术提供了一种非侵入性的、更可伸缩的替代方法。这类专家数据缺乏导致模型选择和特征开发系统时的学习的不确定性。为此,本文件调查了使用特征和模型组合方法提高BERT和Roberta预先培训的有限数据文本编码器域微调的稳健性,然后将由此产生的嵌入功能输入后端分类器组合中,以便通过多数投票产生最后的ADD检测决定。对ADRESS20挑战数据集进行的实验表明,在系统开发过程中使用模型和特征组合取得了一致的性能改进。在由48名老年人组成的ADRESS20测试组中,分别使用手动和ASR语音记录器分别获得91.67%和93.75%的最新ADAD检测分数和93.75%。