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

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arxiv 2502.11128 v2 pith:6N7BSOIR submitted 2025-02-16 cs.CL cs.SDeess.AS

Hui Wang , Shujie Liu , Lingwei Meng , Jinyu Li , Yifan Yang , Shiwan Zhao , Haiyang Sun , Yanqing Liu
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Haoqin Sun Jiaming Zhou Yan Lu Yong Qin
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classification cs.CLcs.SDeess.AS
keywords felleautoregressivecontinuous-valuedflowmatchinglanguagemodelingcoarse-to-fine
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TTA-Bench: A Comprehensive Benchmark for Evaluating Text-to-Audio Models

    cs.SD 2025-09 conditional novelty 6.0 of 10

    TTA-Bench offers a seven-dimension, 2,999-prompt evaluation of ten text-to-audio models with 118,000 human ratings, covering quality, robustness, fairness, bias, and toxicity.

  2. Next Tokens Denoising for Speech Synthesis

    cs.SD 2025-07 conditional novelty 6.0 of 10

    Dragon-FM generates speech autoregressively over two-second chunks while using flow matching inside each chunk, achieving fast synthesis at 12.5 discrete audio tokens per second.

  3. DualDub: Video-to-Soundtrack Generation via Joint Speech and Background Audio Synthesis

    cs.MM 2025-07 conditional novelty 6.0 of 10

    A single multimodal language model can generate intelligible speech and synchronized background audio jointly from a silent video, transcript, and reference voice, outperforming separately concatenated speech and audi...

  4. CLEAR: Continuous Latent Autoregressive Modeling for High-quality and Low-latency Speech Synthesis

    eess.AS 2025-08 conditional novelty 5.0 of 10

    CLEAR is a zero-shot TTS model that autoregressively predicts compact continuous audio latents with a per-token rectified flow head, reaching 1.88% WER on LibriSpeech Subset-B with an RTF of 0.29 and a 96 ms streaming delay.

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