高效计算语音表征中音素区分度的工具库
fastabx: A library for efficient computation of ABX discriminability
- 基于Python构建，支持任意ABX任务的快速搭建
- 可高效计算表征间的距离，加速研究迭代
- 适合语音、多模态等表征学习领域的研究人员
我们提出fastabx，一个高性能的Python库，用于构建ABX判别任务。ABX是衡量感兴趣类别间分离程度的指标，广泛应用于自监督语音表征的音素判别性评估。然而，由于缺乏合适的工具，其应用受到限制。fastabx通过提供可构建任意类型ABX任务的框架，并具备快速计算表征间距离的能力，解决了这一问题。该工具将为表示学习社区提供重要支持，使研究者能系统探究从学习到的表征中可直接提取的信息，涵盖语音以外的多个领域。代码开源地址：https://github.com/bootphon/fastabx。
原文摘要 · Abstract (English)
We introduce fastabx, a high-performance Python library for building ABX discrimination tasks. ABX is a measure of the separation between generic categories of interest. It has been used extensively to evaluate phonetic discriminability in self-supervised speech representations. However, its broader adoption has been limited by the absence of adequate tools. fastabx addresses this gap by providing a framework capable of constructing any type of ABX task while delivering the efficiency necessary for rapid development cycles, both in task creation and in calculating distances between representations. We believe that fastabx will serve as a valuable resource for the broader representation learning community, enabling researchers to systematically investigate what information can be directly extracted from learned representations across several domains beyond speech processing. The source code is available at https://github.com/bootphon/fastabx.
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