Introduction: We present a screening method for early dementia using features based on sound objects as voice biomarkers. Methods: The final dataset used for machine learning models consisted of 266 observations, with a distribution of 186 healthy individuals, 46 diagnosed with Alzheimer's, and 34 with MCI. This method is based on six-second recordings of the sustained vowel /a/ spoken by the subject. The main original contribution of this work is the use of carefully crafted features based on sound objects. This approach allows one to first represent the sound spectrum in a more accurate way than the standard spectrum, and then build interpretable features containing relevant information about subjects' control over their voice. Results: ROC AUC obtained in this work for distinguishing healthy subjects from those with MCI was 0.85, while accuracy was 0.76. For distinguishing between healthy subjects and those with either MCI or Alzheimer's the results were 0.84, 0.77, respectively. Conclusion: The use of features based on sound objects enables screening for early dementia even on very short recordings of language-independent voice samples.

翻译：引言：我们提出了一种利用基于声音对象的特征作为语音生物标志物进行早期痴呆筛查的方法。方法：用于机器学习模型的最终数据集包含266个观测样本，其中健康个体186例、阿尔茨海默病患者46例、轻度认知障碍（MCI）患者34例。该方法基于受试者发出的持续元音/a/的六秒录音。本研究的主要原创贡献在于使用了精心设计的基于声音对象的特征。这一方法首先能够比标准频谱更精确地表示声音频谱，继而构建包含受试者声音控制能力相关信息的可解释特征。结果：本研究在区分健康个体与MCI患者时，ROC AUC达到0.85，准确率为0.76；在区分健康个体与MCI或阿尔茨海默病患者时，两项指标分别为0.84和0.77。结论：即使基于极短且与语言无关的语音样本，基于声音对象的特征也能实现早期痴呆筛查。