Transferring speech-generic and depression-specific knowledge for
  Alzheimer's disease detection
Ziyun Cui Wen Wu Wei-Qiang Zhang Ji Wu Chao Zhang
The detection of Alzheimer's disease (AD) from spontaneous speech has
attracted increasing attention while the sparsity of training data remains an
important issue. This paper handles the issue by knowledge transfer,
specifically from both speech-generic and depression-specific knowledge. The
paper first studies sequential knowledge transfer from generic foundation
models pretrained on large amounts of speech and text data. A block-wise
analysis is performed for AD diagnosis based on the representations extracted
from different intermediate blocks of different foundation models. Apart from
the knowledge from speech-generic representations, this paper also proposes to
simultaneously transfer the knowledge from a speech depression detection task
based on the high comorbidity rates of depression and AD. A parallel knowledge
transfer framework is studied that jointly learns the information shared
between these two tasks. Experimental results show that the proposed method
improves AD and depression detection, and produces a state-of-the-art F1 score
of 0.928 for AD diagnosis on the commonly used ADReSSo dataset.