arXiv (All)
open access
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR
Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate error rates, leaving open why and when a method helps. On four low-resource languages and English at five data sizes, we cross two augmentations that m
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
- CTC-Seeded Token Edit Refinement for Non-Autoregressive Speech Recognition#1249410
- Learning from Real Stuttering: Synthetic Mandarin Stuttering Speech for Data Augmentation in Speech Recognition#1326328
- Phoneme-guided TTS augmentation for ASR: A unified pipeline and multilingual evaluation#1013673
- google/WaxalNLP#786685
- Attacking UTMOS: Probing the Robustness of a Speech Quality Assessment Model#1123149
Record · ID 1298303
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