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.

基于声音对象的特征作为声音生物标志物，提出了一种早期认知功能障碍筛查方法。通过使用机器学习模型对包括声音对象的精心构造特征进行分析，可以对语言无关的声音样本进行短时筛查。