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Voice embedding geometry tracks human similarity better than verification accuracy

huggingface.co by Szu-Chi Chen et al. Editor score 8/10

The study reports a −0.95 rank correlation between embedding effective dimensionality and human perceptual alignment; a dimensionality bottleneck raises alignment from 0.08 to 0.74. It shows why speaker-verification accuracy alone can mislead when evaluating voice similarity in generated speech.

What

A study tests whether speaker-verification models can serve as proxies for human voice similarity.

Method

Perceptual alignment depended more on training objective than EER; effective embedding dimensionality tracked alignment, and a bottleneck raised it from 0.08 to 0.74.

Why it matters

Voice-similarity systems should evaluate embedding geometry and human alignment, rather than treating verification accuracy as a reliable proxy.

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