Summary of Impact Of Speech Mode in Automatic Pathological Speech Detection, by Shakeel A. Sheikh and Ina Kodrasi
Impact of Speech Mode in Automatic Pathological Speech Detection
by Shakeel A. Sheikh, Ina Kodrasi
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 A novel study investigates the impact of speech mode on automatic pathological speech detection methods, particularly focusing on the differences between controlled and spontaneous speech scenarios. The researchers analyze two categories of approaches: classical machine learning and deep learning models. They find that classical methods struggle to identify pathology-discriminant cues in spontaneous speech, while deep learning models excel in capturing additional cues previously inaccessible in non-spontaneous speech. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how automatic pathological speech detection works differently for controlled and spontaneous speech. It compares two types of approaches: machine learning and deep learning. The results show that classical methods have trouble finding clues of pathology in everyday conversations, but deep learning models do well at picking up cues that were hard to find before. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
