Revisiting Lexicon Evaluation in Unsupervised Word Discovery 事件

Revisiting Lexicon Evaluation in Unsupervised Word Discovery arXiv:2606.06183v1 Announce Type: cross Abstract: Building a lexicon from discovered word-like units is a central goal in zero-resource speech processing. But do our evaluations provide a trustworthy indication of lexicon quality? A common metric, normalized edit distance, averages the phoneme edit distances between discovered units in each cluster. We show that this metric has an inherent bias toward the quality of large clusters, in