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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11771 |
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| _version_ | 1866915452426387456 |
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| author | Peckham, Leo Ong, Michael Nagy, Naomi Dunbar, Ewan |
| author_facet | Peckham, Leo Ong, Michael Nagy, Naomi Dunbar, Ewan |
| contents | We examine the role of transcription inconsistencies in the Faetar Automatic Speech Recognition benchmark, a challenging low-resource ASR benchmark. With the help of a small, hand-constructed lexicon, we conclude that find that, while inconsistencies do exist in the transcriptions, they are not the main challenge in the task. We also demonstrate that bigram word-based language modelling is of no added benefit, but that constraining decoding to a finite lexicon can be beneficial. The task remains extremely difficult. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating Transcription Normalization in the Faetar ASR Benchmark Peckham, Leo Ong, Michael Nagy, Naomi Dunbar, Ewan Computation and Language We examine the role of transcription inconsistencies in the Faetar Automatic Speech Recognition benchmark, a challenging low-resource ASR benchmark. With the help of a small, hand-constructed lexicon, we conclude that find that, while inconsistencies do exist in the transcriptions, they are not the main challenge in the task. We also demonstrate that bigram word-based language modelling is of no added benefit, but that constraining decoding to a finite lexicon can be beneficial. The task remains extremely difficult. |
| title | Investigating Transcription Normalization in the Faetar ASR Benchmark |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.11771 |