Alberto Parola
@alpar.bsky.social
Assistant Professor at Centre for Language Technology, Copenhagen Uni. @MSCActions Fellow | Pragmatics, social cognition & mental disorders, NLP, speech analysis, multimodal communication, Bayesian stats |
7/8 🌍 Why does generalization fail?
• Linguistic differences affect how SCZ symptoms relate to acoustic features
• Clinical heterogeneity limits robustness of ML models trained on small, homogenous samples
• Models biased toward general features, not capturing diagnosis- or symptom-specific markers
• Linguistic differences affect how SCZ symptoms relate to acoustic features
• Clinical heterogeneity limits robustness of ML models trained on small, homogenous samples
• Models biased toward general features, not capturing diagnosis- or symptom-specific markers
December 3, 2024 at 4:17 PM
7/8 🌍 Why does generalization fail?
• Linguistic differences affect how SCZ symptoms relate to acoustic features
• Clinical heterogeneity limits robustness of ML models trained on small, homogenous samples
• Models biased toward general features, not capturing diagnosis- or symptom-specific markers
• Linguistic differences affect how SCZ symptoms relate to acoustic features
• Clinical heterogeneity limits robustness of ML models trained on small, homogenous samples
• Models biased toward general features, not capturing diagnosis- or symptom-specific markers
6/8 📢 Key Finding #3:
We tested two alternative approach:
1️) Mixture of Experts models (combining predictions from models trained on different languages, Plot 3).
2) Multi-language training set (combin. training data from multiple languages, Plot 4).
❌ Results: Still near chance level (F1 ~ 0.50).
We tested two alternative approach:
1️) Mixture of Experts models (combining predictions from models trained on different languages, Plot 3).
2) Multi-language training set (combin. training data from multiple languages, Plot 4).
❌ Results: Still near chance level (F1 ~ 0.50).
December 3, 2024 at 4:10 PM
6/8 📢 Key Finding #3:
We tested two alternative approach:
1️) Mixture of Experts models (combining predictions from models trained on different languages, Plot 3).
2) Multi-language training set (combin. training data from multiple languages, Plot 4).
❌ Results: Still near chance level (F1 ~ 0.50).
We tested two alternative approach:
1️) Mixture of Experts models (combining predictions from models trained on different languages, Plot 3).
2) Multi-language training set (combin. training data from multiple languages, Plot 4).
❌ Results: Still near chance level (F1 ~ 0.50).
5/8 🚨 Key Finding
✔️#1: ML models perform when trained/tested on the same language (F1 ~ 0.75) (Plot1)
❌#2: But when trained/tested on different languages (e.g., Danish → Chinese), performance drops significantly (F1 ~ 0.50) (Plot 2).
Cross-linguistic generalizability remains a key challenge!
✔️#1: ML models perform when trained/tested on the same language (F1 ~ 0.75) (Plot1)
❌#2: But when trained/tested on different languages (e.g., Danish → Chinese), performance drops significantly (F1 ~ 0.50) (Plot 2).
Cross-linguistic generalizability remains a key challenge!
December 3, 2024 at 4:06 PM
5/8 🚨 Key Finding
✔️#1: ML models perform when trained/tested on the same language (F1 ~ 0.75) (Plot1)
❌#2: But when trained/tested on different languages (e.g., Danish → Chinese), performance drops significantly (F1 ~ 0.50) (Plot 2).
Cross-linguistic generalizability remains a key challenge!
✔️#1: ML models perform when trained/tested on the same language (F1 ~ 0.75) (Plot1)
❌#2: But when trained/tested on different languages (e.g., Danish → Chinese), performance drops significantly (F1 ~ 0.50) (Plot 2).
Cross-linguistic generalizability remains a key challenge!