  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating
preventive care to delay further progression. This paper presents the
development of a state-of-the-art Conformer based speech recognition system
built on the DementiaBank Pitt corpus for automatic AD detection. The baseline
Conformer system trained with speed perturbation and SpecAugment based data
augmentation is significantly improved by incorporating a set of purposefully
designed modeling features, including neural architecture search based
auto-configuration of domain-specific Conformer hyper-parameters in addition to
parameter fine-tuning; fine-grained elderly speaker adaptation using learning
hidden unit contributions (LHUC); and two-pass cross-system rescoring based
combination with hybrid TDNN systems. An overall word error rate (WER)
reduction of 13.6% absolute (34.8% relative) was obtained on the evaluation
data of 48 elderly speakers. Using the final systems' recognition outputs to
extract textual features, the best-published speech recognition based AD
detection accuracy of 91.7% was obtained.
