Using Phonemes in cascaded S2S translation pipeline
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916702833344512 |
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| author | Pilz, Rene Schneider, Johannes |
| author_facet | Pilz, Rene Schneider, Johannes |
| contents | This paper explores the idea of using phonemes as a textual representation within a conventional multilingual simultaneous speech-to-speech translation pipeline, as opposed to the traditional reliance on text-based language representations. To investigate this, we trained an open-source sequence-to-sequence model on the WMT17 dataset in two formats: one using standard textual representation and the other employing phonemic representation. The performance of both approaches was assessed using the BLEU metric. Our findings shows that the phonemic approach provides comparable quality but offers several advantages, including lower resource requirements or better suitability for low-resource languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Phonemes in cascaded S2S translation pipeline Pilz, Rene Schneider, Johannes Machine Learning Computation and Language Sound Audio and Speech Processing This paper explores the idea of using phonemes as a textual representation within a conventional multilingual simultaneous speech-to-speech translation pipeline, as opposed to the traditional reliance on text-based language representations. To investigate this, we trained an open-source sequence-to-sequence model on the WMT17 dataset in two formats: one using standard textual representation and the other employing phonemic representation. The performance of both approaches was assessed using the BLEU metric. Our findings shows that the phonemic approach provides comparable quality but offers several advantages, including lower resource requirements or better suitability for low-resource languages. |
| title | Using Phonemes in cascaded S2S translation pipeline |
| topic | Machine Learning Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.16234 |