arXiv (All)
open access
Language Orthogonalization of Self-Supervised Speech Representations for Cross-lingual Parkinson's Detection
Self-supervised speech models (S3Ms) provide powerful representations for Parkinson's disease (PD) detection, making cross-lingual transfer attractive for languages lacking labeled patient speech. However, these representations also encode language identity, which can confound this transfer: without
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Structure-Aware Masking for Protein Representation Learning#1110240
- Repurposing Pre-trained LLMs as High Fidelity Continuous Text Autoencoders#1063459
- Learning Task-Specific Antibody Representations via Function-Aware Masking#819961
- What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization#665632
- What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization#664132
Record · ID 987240
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive