dynamic-time-warping-58e005a1·2 events·first seen Aliases: Dynamic Time Warping
Researchers propose a text-free framework for second-language (L2) speech assessment using Dynamic Time Warping (DTW) over self-supervised WavLM representations, covering phonetic accuracy, rhythm, and intonation in English and Japanese. The DTW-based approach comparing learner speech to native templates exceeds human agreement on holistic phonetic scoring, and a novel warping-path method approaches human-level rhythm assessment. Intonation scoring, combining DTW over prosodic residuals with pitch and intensity features, shows more modest results. The method requires no labeled L2 data, making it applicable in low-resource settings.
EpiCurveBench introduces a benchmark of 1,000 real-world epidemic curve images and a new evaluation metric (EpiCurveSimilarity, ECS) designed to assess vision-language models on time-series chart extraction, addressing limitations of existing metrics that ignore temporal structure. Evaluating six methods including three frontier closed VLMs, one open VLM, and two specialized chart-extraction systems, the best model achieves only 52.3% ECS, revealing substantial headroom compared to saturating scores on ChartQA. ECS is validated against downstream epidemiological statistics and shown to correlate 1.5–3.6× more strongly than Dynamic Time Warping across four summary metrics. The benchmark targets the public-health use case of digitizing historical outbreak data trapped in published figures, but generalizes to any structured time-series chart-extraction task.