通过对比边界松弛后的对齐路径,判断动态时间规整的局部可靠性。
Estimating the Reliability of Dynamic Time Warping Alignments Using Circumstantial Evidence

- 用柔性边界重跑对齐,比较原路径与新路径一致性。
- 在音频对齐任务中,可靠区域识别的AUROC达0.97。
- 无需标注数据,适合评估任意DTW对齐结果的可信度。
近期研究尝试通过可微分的DTW变体(如Soft-DTW)处理动态时间规整(DTW)对齐路径中的不确定性。本文提出一种新方法:针对给定的DTW对齐路径,设计一个指标来评估其局部段的可靠性。该方法基于旁证思想——若某路径明显突出,即使放宽边界条件重新对齐,仍会保持一致;反之,若路径较弱,则放松边界后可能改变。具体地,选取对齐路径的一段局部区域,使用允许边界灵活调整的FlexDTW重新估计对齐,并测量原始路径与新路径的一致性作为可靠性度量。我们在多个场景下的音频-音频对齐任务中评估该指标,涵盖匹配与非匹配区域,结果显示该指标在整体上实现0.97的AUROC。该方法为无监督评估DTW对齐路径可靠性提供了有效手段。
原文摘要 · Abstract (English)
Recent works have explored ways to handle uncertainty in dynamic time warping (DTW) alignment paths through the use of differentiable variants of DTW like Soft-DTW. In this paper, we approach the issue of uncertainty in DTW alignment paths in a different way. Given a DTW alignment path, we propose a metric that indicates how reliable a local segment of the alignment path is. The intuition for our metric is based on the idea of circumstantial evidence. If DTW has found a very prominent path, then if we re-run the alignment with relaxed boundary conditions, it will still pick the same path. If, on the other hand, DTW has found a "weak" path, then re-running the alignment with relaxed boundary conditions will likely yield a different path. Accordingly, our reliability metric is computed by picking a local section of the DTW alignment path, re-estimating the alignment with FlexDTW (which allows flexibility in the boundary conditions), and then measuring how well the DTW and FlexDTW paths agree. We assess the proposed reliability metric on DTW alignment paths containing both matching and non-matching regions across a range of scenarios on an audio-audio alignment task. We find that the reliability metric correctly identifies reliable regions of the alignment path with an aggregate AUROC of 0.97. This approach provides an unsupervised method for estimating the reliability of a DTW alignment path.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。