Alzheimers Dementia Recognition through Spontaneous Speech (ADReSS)

Wed-SS-1-6-6 A Comparison of Acoustic and Linguistics Methodologies for Alzheimer's Dementia Recognition

Nicholas Cummins(University of Augsburg), Yilin Pan(University of Sheffield), Zhao Ren(University of Augsburg), Julian Fritsch(Idiap Research Institute), Venkata Srikanth Nallanthighal(Philips Research, Eindhoven and Radboud University, Nijmegen), Heidi Christensen(University of Sheffield), Daniel Blackburn(University of Sheffield), Björn Schuller(University of Augsburg / Imperial College London), Mathew Magimai Doss(Idiap Research Institute), Helmer Strik(Centre for Language and Speech Technology (CLST), Centre for Language Studies (CLS), Radboud University Nijmegen) and Aki Harma(Philips Research)
Abstract: IIn the light of the current COVID-19 pandemic, the need for remote digital health assessment tools is greater than ever. This statement is especially pertinent for elderly and vulnerable populations. In this regard, the INTERSPEECH 2020 Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) Challenge offers competitors the opportunity to develop speech and language-based systems for the task of Alzheimer's Dementia (AD) recognition. The challenge data consists of speech recordings and their transcripts, the work presented herein is an assessment of different contemporary approaches on these modalities. Specifically, we compared a hierarchical neural network with an attention mechanism trained on linguistic features with three acoustic-based systems: (i) Bag-of-Audio-Words (BoAW) quantising different low-level descriptors, (ii) a Siamese Network trained on log-Mel spectrograms, and (iii) a Convolutional Neural Network (CNN) end-to-end system trained on raw waveforms. Key results indicate the strength of the linguistic approach over the acoustics systems. Our strongest test-set result was achieved using a late fusion combination of BoAW, End-to-End CNN, and hierarchical-attention networks, which outperformed the challenge baseline in both the classification and regression tasks.
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