---
title: "Zhu 2021"
type: concept
related: [Ctp, Shah 2021, Martinc 2021, Mmse]
source: https://www.jemoka.com/posts/kbhzhu_2021/
confidence: high
status: active
---

DOI: 10.3389/fcomp.2021.624683
One-Liner late fusion of multimodal signal on the CTP task using transformers, mobilnet, yamnet, and mockingjay
Novelty Similar to Martinc 2021 and Shah 2021 but actually used the the current Neural-Network state of the art Used late fusion again after the base model training Proposed that inconsistency in the diagnoses of MMSE scores could be a great contributing factor to multi-task learning performance hindrance Notable Methods Proposed base model for transfer learning from text based on MobileNet (image), YAMNet (audio), Mockingjay (speech) and BERT (text) Data all sourced from recording/transcribing/recognizing CTP task Key Figs Figure 3 and 4 This figure tells us the late fusion architecture used
Table 2 Pre-training with an existing dataset had (not statistically quantified) improvement against a randomly seeded model.
Table 3 Concat/Add fusion methods between audio and text provided even better results; confirms Martinc 2021 on newer data
