Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech
Abstract
Metric-induced discrete flow matching (MI-DFM) exploits token-latent geometry for discrete generation, but its practical use is limited by two issues: heuristic schedulers requiring hyperparameter search, and finite-step path-tracking error from its first-order continuous-time Markov chain (CTMC) solver. We address both issues. First, we derive a kinetic-optimal scheduler for prescribed scalar-parameterized probability paths, and instantiate it for MI-DFM as a training-free numerical schedule that traverses the path at constant Fisher-Rao speed. Second, we introduce a finite-step moment correction that adjusts the jump probability while preserving the CTMC jump destination distribution. We validate the resulting method, GibbsTTS, on codec-based zero-shot text-to-speech (TTS). Under controlled comparisons with a unified architecture and large-scale dataset, GibbsTTS achieves the best objective naturalness and is preferred in subjective evaluations over masked discrete generative baselines. Additionally, in comparison with the evaluated state-of-the-art TTS systems, GibbsTTS shows strong speaker similarity, achieving the highest similarity on three of four test sets and ranking second on the fourth. Project page: https://ydqmkkx.github.io/GibbsTTSProject
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to improving zero-shot text-to-speech systems through innovative scheduling and correction techniques. The technical contributions are significant, addressing key limitations in existing methodologies and demonstrating strong experimental results.
Methodology
The paper introduces a kinetic-optimal scheduler and a finite-step moment correction for metric-induced discrete flow matching (MI-DFM) in zero-shot text-to-speech (TTS). The methodology is well-structured, addressing two significant limitations of MI-DFM: the reliance on heuristic schedulers and finite-step path-tracking errors. The kinetic-optimal scheduler is derived from Fisher-Rao geometry, providing a training-free numerical approach that avoids hyperparameter tuning. The moment correction effectively mitigates discretization errors in CTMC sampling. Overall, the methods are innovative and present a clear advancement in the field of TTS.
Results & evaluation
The experiments are robust, utilizing a large-scale dataset for evaluation and controlled comparisons against state-of-the-art systems. The results demonstrate that GibbsTTS achieves superior objective naturalness and speaker similarity metrics, with subjective evaluations further validating the model's performance. The paper effectively communicates the experimental setup and results, providing a comprehensive analysis of the proposed methods.
Reproducibility
The paper provides sufficient detail on the model architecture, training procedures, and evaluation metrics, which facilitates reproducibility. However, the lack of a public code repository or demo limits the ability for others to fully replicate the results.
Limitations
The primary limitation is the focus on zero-shot TTS, which may restrict the generalizability of the proposed methods to other domains. Additionally, the reliance on a specific codec and the absence of exploration into alternative distance metrics for token embeddings could limit the applicability of the findings.
Broader impact
The advancements in TTS technology have significant implications for applications in accessibility, entertainment, and human-computer interaction. The proposed methods could enhance the naturalness and speaker similarity in synthesized speech, potentially improving user experiences in various applications. The paper presents a novel approach to improving zero-shot text-to-speech systems through innovative scheduling and correction techniques. The technical contributions are significant, addressing key limitations in existing methodologies and demonstrating strong experimental results.
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