Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity

Abstract:Modeling speech variation is key to natural, expressive generation. Speaker embeddings are commonly used to condition personalized speech systems, but they are typically trained for speaker recognition, where intra-speaker variability is suppressed and inter-speaker separation is maximized. This objective leads to overly compact representations that may discard variations crucial for generation. We revisit this design choice and propose a sub-center modeling framework for speaker embeddings. Instead of a single prototype per speaker, we learn multiple sub-centers during discriminative training, allowing utterances to align with different prototypes. This strategy preserves structured intra-speaker variability while maintaining discriminability. In zero-shot voice conversion, our method improves intelligibility, increases pitch variability, achieves higher naturalness ratings, and retains strong speaker verification performance.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)

arXiv:2407.04291 [eess.AS]

(or arXiv:2407.04291v4 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2407.04291 Focus to learn more arXiv-issued DOI via DataCite

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