arXiv (All)
open access
Fairness Beyond a Single Run: Training-Seed Variability in Speech LLM Adaptation
Demographic fairness gaps in automatic speech recognition are almost always reported from a single training run. We fine-tune the Q-former projector and LoRA adapters of a speech LLM at five audio compression factors and six random seeds, holding the encoder, base decoder, data and decoding fixed, a
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
- Speech Block Influence: Component-Specific Layer Scoring for Pruning Speech LLMs#1077272
- DriftAudio: Marginal Drifting for Distributional Post-Training of One-Step Text-to-Audio Generators#1064142
- Official Review · ICLR 2025#787129
- Auditing Generative Audio Calls for Known-Task Audio-LLM Evaluatio#1083182
- Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning#1070549
Record · ID 1143943
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