Conference Paper/Proceeding/Abstract 202 views
Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs
Morteza Rohanian,
Julian Hough 
,
Matthew Purver
,
Matthew Purver
Interspeech 2021
Swansea University Author:
Julian Hough
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.21437/interspeech.2021-1633
Abstract
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,...
| Published in: | Interspeech 2021 |
|---|---|
| Published: |
ISCA
ISCA
2021
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64932 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
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. |
|---|---|
| College: |
Faculty of Science and Engineering |