#Ntj
Last edited: June 6, 2022
DOI: 10.3389/fcomp.2021.642633
One-Liner Developed a kitchen sink of diagnoses tools and correlated it with biomarkers.
Novelty The kitchen sink of data collection (phones, tablet, eye tracker, microphone, wristband) and the kitchen sink of noninvasive data imaging, psych, speech assesment, clinical metadata.
Notable Methods Here’s their kitchen sink
I have no idea why a thermal camera is needed
Key Figs Here are the features they extracted
Developed the features collected via a method similar to action research, did two passes and refined/added information after preliminary analysis. Last edited: June 6, 2022
DOI: 10.3389/fcomp.2021.624694
One-Liner Proposed a large multimodal approach to embed auditory info + biomarkers for baseline classification.
Novelty Developed a massively multimodal audio-to-embedding correlation system that maps audio to biomarker information collected (mood, memory, respiratory) and demonstrated its ability to discriminate cough results for COVID. (they were looking for AD; whoopsies)
Notable Methods Developed a feature extraction model for AD detection named Open Voice Brain Model Collected a dataset on people coughing and correlated it with biomarkers Key Figs Figure 2 This is MULTI-MODAL as heck Last edited: June 6, 2022
DOI: 10.3389/fnagi.2021.642647
One-Liner Combined bag-of-words on transcript + ADR on audio to various classifiers for AD; ablated BERT’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. Last edited: June 6, 2022
DOI: 10.3389/fcomp.2021.624558
One-Liner analyzed spontaneous speech transcripts (only!) from TD and AD patients with fastText and CNN; best was \(83.33\%\) acc.
Novelty threw the NLP kitchen sink to transcripts fastText CNN (with vary n-gram kernel 2,3,4,5 sizes) Notable Methods embeddings seaded by GloVe fastText are much faster, but CNN won out Key Figs the qual results PAR (participant), INV (investigator)
Notes Hey look a review of the field: Last edited: June 6, 2022
DOI: 10.3389/fcomp.2021.624659
One-Liner Multi-feature late fusion of NLP results (by normalizing text and n-gram processing) with OpenSMILE embedding results.
Novelty NLP transcript normalization (see methods) and OpenSMILE; otherwise similar to Martinc 2021. Same gist but different data-prep.
Notable Methods N-gram processed the input features Used WordNet to replace words with roots Key Figs New Concepts OpenSMILE