Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity
TL;DR
This work revisits this design choice and proposes a sub-center modeling framework for speaker embeddings, which improves intelligibility, increases pitch variability, achieves higher naturalness ratings, and retains strong speaker verification performance in zero-shot voice conversion.
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.
