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arXiv · 2511.09995

Time-Layer Adaptive Alignment for Speaker Similarity in Flow-Matching Based Zero-Shot TTS

Haoyu Li·Mingyang Han·Yu Xi·Dongxiao Wang·Hankun Wang·Haoxiang Shi·Boyu Li·Jun Song·Bo Zheng·Shuai Wang·Kai Yu
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Abstract

Flow-Matching (FM)-based zero-shot text-to-speech (TTS) systems exhibit high-quality speech synthesis and robust generalization capabilities. However, the speaker representation ability of such systems remains underexplored, primarily due to the lack of explicit speaker-specific supervision in the FM framework. To this end, we conduct an empirical analysis of speaker information distribution and reveal its non-uniform allocation across time steps and network layers, underscoring the need for adaptive speaker alignment. Accordingly, we propose Time-Layer Adaptive Speaker Alignment (TLA-SA), a strategy that enhances speaker consistency by jointly leveraging temporal and hierarchical variations. Experimental results show that TLA-SA substantially improves speaker similarity over baseline systems on both research- and industrial-scale datasets and generalizes well across diverse model architectures, including decoder-only language model (LM)-based and free TTS systems. A demo is provided.

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Haoyu Li, Mingyang Han, Yu Xi, Dongxiao Wang, Hankun Wang, Haoxiang Shi, Boyu Li, Jun Song, Bo Zheng, Shuai Wang, Kai Yu. 2025-11-13. Time-Layer Adaptive Alignment for Speaker Similarity in Flow-Matching Based Zero-Shot TTS. https://arxiv.org/abs/2511.09995

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