使用间接证据估计动态时间规整比对的可靠性
Estimating the Reliability of Dynamic Time Warping Alignments Using Circumstantial Evidence
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中文总结 AI 辅助
研究DTW比对路径不确定性问题，提出基于间接证据的可靠性度量指标，通过FlexDTW重新估计比对并衡量路径一致性来计算指标，在音频比对任务中评估，能无监督地准确估计DTW比对路径可靠性。
中文摘要 AI 辅助
近期研究探索了通过使用如Soft-DTW等可微变体来处理动态时间规整（DTW）比对路径中的不确定性。本文以不同方式处理DTW比对路径中的不确定性问题。给定DTW比对路径，提出一种度量指标来指示比对路径局部片段的可靠性，其基于间接证据的概念。通过选取DTW比对路径的局部片段，用FlexDTW重新估计比对（允许边界条件灵活），然后衡量DTW和FlexDTW路径的一致性来计算可靠性指标。在音频-音频比对任务的一系列场景中，对包含匹配和不匹配区域的DTW比对路径评估该指标，发现其聚合AUROC为0.97，能正确识别可靠区域，提供了一种无监督估计DTW比对路径可靠性的方法。
英文摘要
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.
