Summary of Exploring Gender-specific Speech Patterns in Automatic Suicide Risk Assessment, by Maurice Gerczuk et al.
Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment
by Maurice Gerczuk, Shahin Amiriparian, Justina Lutz, Wolfgang Strube, Irina Papazova, Alkomiet Hasan, Björn W. Schuller
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a speech-based approach for automatic suicide risk assessment to bridge the gap in emergency medicine where patients at risk of suicide often experience delayed access to specialized psychiatric care. The study involves a novel dataset of 20 patients reading neutral texts, and extracts four speech representations encompassing interpretable and deep features. The authors explore the impact of gender-based modelling and phrase-level normalisation on the accuracy of suicide risk assessment, with balanced accuracy of 81% using gender-exclusive modelling and an emotion fine-tuned wav2vec2.0 model. The analysis reveals a discrepancy in the relationship between speech characteristics and suicide risk between female and male subjects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using speech to help doctors quickly figure out if someone might be at risk of taking their own life. This can make a big difference because sometimes people who are suicidal don’t get the help they need right away. The researchers created a special dataset with recordings of 20 people reading neutral texts, and then used that data to create four different ways to measure how someone is speaking. They also tried using gender-specific models and found that this helped them accurately predict suicide risk for men, but not women. This could be important because it shows that the same speech patterns can mean different things for different people. |
