Summary of Not All Errors Are Equal: Investigation Of Speech Recognition Errors in Alzheimer’s Disease Detection, by Jiawen Kang et al.
Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer’s Disease Detection
by Jiawen Kang, Junan Li, Jinchao Li, Xixin Wu, Helen Meng
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the impact of Automatic Speech Recognition (ASR) transcription errors on Alzheimer’s disease (AD) detection using BERT-based systems. Recent studies have shown that ASR recognition errors do not always negatively affect AD detection performance, and this study aims to understand why. The authors analyzed the effects of different types of errors and found that certain words, such as stopwords, while common, do not significantly impact AD detection accuracy. In contrast, keyword-related words play a more crucial role in distinguishing AD cases from controls. This research provides valuable insights into the interplay between ASR errors and downstream detection models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how mistakes in speech recognition affect detecting Alzheimer’s disease using a special kind of artificial intelligence called BERT. Researchers found that not all errors are equal, some words like “the” or “and” don’t really matter when it comes to finding Alzheimer’s disease. However, words related to the diagnosis tasks, like symptoms and test results, make a big difference. |
Keywords
» Artificial intelligence » Bert
