Exploring linguistic feature and model combination for speech recognition based automatic AD detection
This work addresses early AD screening for elderly patients using speech, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of improving Alzheimer's disease detection from speech using limited data by combining features and models, achieving state-of-the-art accuracies of 91.67% and 93.75% on manual and ASR transcripts respectively.
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.