FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine Flow Matching

• URL: http://arxiv.org/abs/2502.11128v2
• Date: Wed, 03 Sep 2025 01:59:58 GMT
• Title: FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine Flow Matching
• Authors: Hui Wang, Shujie Liu, Lingwei Meng, Jinyu Li, Yifan Yang, Shiwan Zhao, Haiyang Sun, Yanqing Liu, Haoqin Sun, Jiaming Zhou, Yan Lu, Yong Qin,
• Abstract summary: FELLE is an autoregressive model that integrates language modeling with token-wise flow matching.<n>For each continuous-valued token, FELLE modifies the general prior distribution in flow matching by incorporating information from the previous step.<n>FELLE generates continuous-valued tokens hierarchically, conditioned on the language model's output.
• Score: 56.30231216917128
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To advance continuous-valued token modeling and temporal-coherence enforcement, we propose FELLE, an autoregressive model that integrates language modeling with token-wise flow matching. By leveraging the autoregressive nature of language models and the generative efficacy of flow matching, FELLE effectively predicts continuous-valued tokens (mel-spectrograms). For each continuous-valued token, FELLE modifies the general prior distribution in flow matching by incorporating information from the previous step, improving coherence and stability. Furthermore, to enhance synthesis quality, FELLE introduces a coarse-to-fine flow-matching mechanism, generating continuous-valued tokens hierarchically, conditioned on the language model's output. Experimental results demonstrate the potential of incorporating flow-matching techniques in autoregressive mel-spectrogram modeling, leading to significant improvements in TTS generation quality, as shown in https://aka.ms/felle.

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