A perceptual listening audit of 2,280 utterance pairs sets a cosine-similarity threshold of 0.354 for removing likely different-speaker utterances from Common Voice client IDs.
To quantify heterogeneity, we used cosine similarity calculated from the ResNet-293 model pre-trained on the mul- tilingual V oxBlink2 dataset (Figure 1)
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Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis
A perceptual listening audit of 2,280 utterance pairs sets a cosine-similarity threshold of 0.354 for removing likely different-speaker utterances from Common Voice client IDs.