🤖 AI Summary
This work addresses the lack of reliability assessment in local alignments produced by Dynamic Time Warping (DTW). It introduces the first unsupervised reliability metric capable of identifying trustworthy alignment regions without requiring ground-truth labels. The method leverages FlexDTW—a variant of DTW with relaxed boundary constraints—to generate alternative alignment paths, which are then compared against the original DTW path to compute a local consistency-based reliability score. Evaluated on audio-to-audio alignment tasks, the approach demonstrates strong performance across diverse scenarios, achieving an average AUROC of 0.97 and effectively quantifying the uncertainty inherent in DTW alignments.
📝 Abstract
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.