Abstract
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.
Abstract (translated)
自监督语音模型（S3Ms）的语音表征已知对音位对比敏感，但其对韵律对比的敏感性尚未被直接测量。ABX辨别任务已通过最小对立对用于测量S3M表征中的音位对比。我们引入了韵律ABX，这是该框架的扩展，仅用少量示例且无需显式标签即可评估韵律对比。此外，我们构建并发布了一个英语和日语最小对立对数据集，并将其与一个普通话数据集结合使用，以评估英语重音、日语音高重音和普通话声调的对比。最后，我们表明模型和层级的排名通常在多种实验条件下保持一致，使其在低资源场景中具有实用价值。
URL
https://arxiv.org/abs/2604.02102
