PRiSM: Benchmarking Phone Realization in Speech Models
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908776447082496 |
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| author | Bharadwaj, Shikhar Li, Chin-Jou Kim, Yoonjae Choi, Kwanghee Yeo, Eunjung Shim, Ryan Soh-Eun Zhou, Hanyu Boldt, Brendon Jacome, Karen Rosero Chang, Kalvin Agrawal, Darsh Xu, Keer Yang, Chao-Han Huck Zhu, Jian Watanabe, Shinji Mortensen, David R. |
| author_facet | Bharadwaj, Shikhar Li, Chin-Jou Kim, Yoonjae Choi, Kwanghee Yeo, Eunjung Shim, Ryan Soh-Eun Zhou, Hanyu Boldt, Brendon Jacome, Karen Rosero Chang, Kalvin Agrawal, Darsh Xu, Keer Yang, Chao-Han Huck Zhu, Jian Watanabe, Shinji Mortensen, David R. |
| contents | Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in developing PR systems, current evaluations only measure surface-level transcription accuracy. We introduce PRiSM, the first open-source benchmark designed to expose blind spots in phonetic perception through intrinsic and extrinsic evaluation of PR systems. PRiSM standardizes transcription-based evaluation and assesses downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. We find that diverse language exposure during training is key to PR performance, encoder-CTC models are the most stable, and specialized PR models still outperform Large Audio Language Models. PRiSM releases code, recipes, and datasets to move the field toward multilingual speech models with robust phonetic ability: https://github.com/changelinglab/prism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14046 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PRiSM: Benchmarking Phone Realization in Speech Models Bharadwaj, Shikhar Li, Chin-Jou Kim, Yoonjae Choi, Kwanghee Yeo, Eunjung Shim, Ryan Soh-Eun Zhou, Hanyu Boldt, Brendon Jacome, Karen Rosero Chang, Kalvin Agrawal, Darsh Xu, Keer Yang, Chao-Han Huck Zhu, Jian Watanabe, Shinji Mortensen, David R. Computation and Language Sound Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in developing PR systems, current evaluations only measure surface-level transcription accuracy. We introduce PRiSM, the first open-source benchmark designed to expose blind spots in phonetic perception through intrinsic and extrinsic evaluation of PR systems. PRiSM standardizes transcription-based evaluation and assesses downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. We find that diverse language exposure during training is key to PR performance, encoder-CTC models are the most stable, and specialized PR models still outperform Large Audio Language Models. PRiSM releases code, recipes, and datasets to move the field toward multilingual speech models with robust phonetic ability: https://github.com/changelinglab/prism. |
| title | PRiSM: Benchmarking Phone Realization in Speech Models |
| topic | Computation and Language Sound |
| url | https://arxiv.org/abs/2601.14046 |