A factor-partitioned embedding framework maps speech utterances to vectors with subspaces for distinct attributes, supporting signed weighted similarity retrieval that can suppress or emphasize specific factors like speaker identity.
Generalized end- to-end loss for speaker verification,
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Multi-Axis Speech Similarity via Factor-Partitioned Embeddings
A factor-partitioned embedding framework maps speech utterances to vectors with subspaces for distinct attributes, supporting signed weighted similarity retrieval that can suppress or emphasize specific factors like speaker identity.