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

• URL: http://arxiv.org/abs/2407.04291v3
• Date: Thu, 18 Sep 2025 20:22:33 GMT
• Title: Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity
• Authors: Ismail Rasim Ulgen, John H. L. Hansen, Carlos Busso, Berrak Sisman,
• Abstract summary: We propose a novel speaker embedding network that employs multiple sub-centers per speaker class during training.<n>This sub-center modeling allows the embedding to capture a broader range of speaker-specific variations while maintaining speaker classification performance.
• Score: 51.250471760075165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling the rich prosodic variations inherent in human speech is essential for generating natural-sounding speech. While speaker embeddings are commonly used as conditioning inputs in personalized speech generation, they are typically optimized for speaker recognition, which encourages the loss of intra-speaker variation. This strategy makes them suboptimal for speech generation in terms of modeling the rich variations at the output speech distribution. In this work, we propose a novel speaker embedding network that employs multiple sub-centers per speaker class during training, instead of a single center as in conventional approaches. This sub-center modeling allows the embedding to capture a broader range of speaker-specific variations while maintaining speaker classification performance. We demonstrate the effectiveness of the proposed embeddings on a voice conversion task, showing improved naturalness and prosodic expressiveness in the synthesized speech.

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