  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.
