---
title: "Martinc 2021"
source: https://www.jemoka.com/posts/kbhmartinc_2021/
tags: [ntj]
---

DOI: 10.3389/fnagi.2021.642647
One-Liner Combined bag-of-words on transcript + ADR on audio to various classifiers for AD; ablated BERT&rsquo;s decesion space for attention to make more easy models in the future.
Novelty Pre-processed each of the two modalities before fusing it (late fusion) Archieved \(93.75\%\) accuracy on AD detection The data being forced-aligned and fed with late fusion allows one to see what sounds/words the BERT model was focusing on by just focusing on the attention on the words Notable Methods Used classic cookie theft data bag of words to do ADR but for words multimodality but late fusion with one (hot-swappable) classifier Key Figs How they did it This is how the combined the forced aligned (:tada:) audio and transcript together.
Bertbelation Ablated BERT results.
The model overall tends to focus on early parts of sentences. y is attention weight, x is position in sentence, blue is TD, red is AD.
New Concepts Active Data Representation
