Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911563104911360 |
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| author | Sun, Haitong McIntosh, Stephen Choi, Kwanghee Yeo, Eunjung Saito, Daisuke Minematsu, Nobuaki |
| author_facet | Sun, Haitong McIntosh, Stephen Choi, Kwanghee Yeo, Eunjung Saito, Daisuke Minematsu, Nobuaki |
| contents | Speech representations from self-supervised speech models (S3Ms) are known to be sensitive to phonemic contrasts, but their sensitivity to prosodic contrasts has not been directly measured. The ABX discrimination task has been used to measure phonemic contrast in S3M representations via minimal pairs. We introduce prosodic ABX, an extension of this framework to evaluate prosodic contrast with only a handful of examples and no explicit labels. Also, we build and release a dataset of English and Japanese minimal pairs and use it along with a Mandarin dataset to evaluate contrast in English stress, Japanese pitch accent, and Mandarin tone. Finally, we show that model and layer rankings are often preserved across several experimental conditions, making it practical for low-resource settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02102 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations Sun, Haitong McIntosh, Stephen Choi, Kwanghee Yeo, Eunjung Saito, Daisuke Minematsu, Nobuaki Computation and Language Machine Learning Sound Audio and Speech Processing Speech representations from self-supervised speech models (S3Ms) are known to be sensitive to phonemic contrasts, but their sensitivity to prosodic contrasts has not been directly measured. The ABX discrimination task has been used to measure phonemic contrast in S3M representations via minimal pairs. We introduce prosodic ABX, an extension of this framework to evaluate prosodic contrast with only a handful of examples and no explicit labels. Also, we build and release a dataset of English and Japanese minimal pairs and use it along with a Mandarin dataset to evaluate contrast in English stress, Japanese pitch accent, and Mandarin tone. Finally, we show that model and layer rankings are often preserved across several experimental conditions, making it practical for low-resource settings. |
| title | Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations |
| topic | Computation and Language Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2604.02102 |