BlasBench: An Open Benchmark for Irish Speech Recognition
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918451753254912 |
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| author | Raj, Jyoutir Conway, John |
| author_facet | Raj, Jyoutir Conway, John |
| contents | Existing multilingual benchmarks include Irish among dozens of languages but apply no Irish-aware text normalisation, leaving reliable and reproducible ASR comparison impossible. We introduce BlasBench, an open evaluation harness that provides a standalone Irish-aware normaliser preserving fadas, lenition, and eclipsis; a reproducible scoring harness and per-utterance predictions released for all evaluated runs. We pilot this by benchmarking 12 systems across four architecture families on Common Voice ga-IE and FLEURS ga-IE. All Whisper variants exceed 100% WER through insertion-driven hallucination. Microsoft Azure reaches 22.2% WER on Common Voice and 57.5% on FLEURS; the best open model, Omnilingual ASR 7B, reaches 30.65% and 39.09% respectively. Models fine-tuned on Common Voice degrade 33-43 points moving to FLEURS, while massively multilingual models degrade only 7-10 - a generalisation gap that single-dataset evaluation misses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10736 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BlasBench: An Open Benchmark for Irish Speech Recognition Raj, Jyoutir Conway, John Computation and Language Sound I.2.7 Existing multilingual benchmarks include Irish among dozens of languages but apply no Irish-aware text normalisation, leaving reliable and reproducible ASR comparison impossible. We introduce BlasBench, an open evaluation harness that provides a standalone Irish-aware normaliser preserving fadas, lenition, and eclipsis; a reproducible scoring harness and per-utterance predictions released for all evaluated runs. We pilot this by benchmarking 12 systems across four architecture families on Common Voice ga-IE and FLEURS ga-IE. All Whisper variants exceed 100% WER through insertion-driven hallucination. Microsoft Azure reaches 22.2% WER on Common Voice and 57.5% on FLEURS; the best open model, Omnilingual ASR 7B, reaches 30.65% and 39.09% respectively. Models fine-tuned on Common Voice degrade 33-43 points moving to FLEURS, while massively multilingual models degrade only 7-10 - a generalisation gap that single-dataset evaluation misses. |
| title | BlasBench: An Open Benchmark for Irish Speech Recognition |
| topic | Computation and Language Sound I.2.7 |
| url | https://arxiv.org/abs/2604.10736 |