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We present two multimodal fusion-based deep learning models that consume ASRtranscribed speech and acoustic data simultaneously to classify whether aspeaker in a structured diagnostic task has Alzheimer's Disease and to whatdegree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTMwith highway layers using words, word probabilities, disfluency features, pauseinformation, and a variety of acoustic features, achieves an accuracy of 84%and RSME error prediction of 4.26 on MMSE cognitive scores. While predictingcognitive decline is more challenging, our models show improvement using themultimodal approach and word probabilities, disfluency and pause informationover word-only models. We show considerable gains for AD classification usingmultimodal fusion and gating, which can effectively deal with noisy inputs fromacoustic features and ASR hypotheses.

Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs