Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and
  Speech Pause Features Robust to Noisy Inputs   We present two multimodal fusion-based deep learning models that consume ASR
transcribed speech and acoustic data simultaneously to classify whether a
speaker in a structured diagnostic task has Alzheimer's Disease and to what
degree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTM
with highway layers using words, word probabilities, disfluency features, pause
information, and a variety of acoustic features, achieves an accuracy of 84%
and RSME error prediction of 4.26 on MMSE cognitive scores. While predicting
cognitive decline is more challenging, our models show improvement using the
multimodal approach and word probabilities, disfluency and pause information
over word-only models. We show considerable gains for AD classification using
multimodal fusion and gating, which can effectively deal with noisy inputs from
acoustic features and ASR hypotheses.

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The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
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The categories of this paper are: chem-ph
3
The categories of this paper are: physics
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The categories of this paper are: funct-an
2
The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph