1.
Machine Learning Methods in
Detecting Alzheimer’s Disease
from Speech and Language

2.
Alzheimer’s Disease (AD)
●
●
●
●

3.
Winterlight’s Assessment Tools
Placeholder for the video

4.
Extracting Features from Speech
●
●
●
the boy is handing the girl &uh cookies and she's telling
him to be quiet i guess.

5.
Consensus Networks: Motivation
●
○
○
●

6.
Consensus Networks: Framework
Z. Zhu, J. Novikova, and F. Rudzicz. Detecting cognitive impairments by agreeing on interpretations of linguistic features. In Proceedings of NAACL,
2019

7.
Consensus Networks: Algorithm

8.
Model Characteristics

9.
Benchmarks and Semi-supervised Learning
Z. Zhu, J. Novikova, and F. Rudzicz. Semi-supervised classification by reaching consensus among modalities. In: NeurIPS Workshop on
Interpretability and Robustness in Audio, Speech, and Language IRASL, Montreal, 2018

10.
Effect of Automatic Speech Recognition
Placeholder for the video

11.
ASR in Winterlight’s Assessment

12.
Effect of ASR Errors on Classification
J. Novikova, A. Balagopalan, K. Shkaruta and F. Rudzicz. Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power.
In: The 5th Workshop on Noisy User-generated Text at EMNLP, Hong Kong, 2019
●
●

13.
Take Away
●
●
●
●
●
●
●
●

14.
Thank you!
Questions?