DMOSpeech 2：面向度量优化语音合成中时长预测的强化学习
DMOSpeech 2: Reinforcement Learning for Duration Prediction in Metric-Optimized Speech Synthesis
AI总结：
DMOSpeech 2通过强化学习将度量优化扩展到时长预测器，并引入教师引导采样，在保持质量的同时将采样步骤减半，构建了更完整的度量优化语音合成流程。
摘要：
基于扩散的文本到语音（TTS）系统在零样本语音合成方面取得了显著进展，但针对感知度量优化所有组件仍然具有挑战性。先前使用DMOSpeech的工作证明了针对语音生成组件的直接度量优化，但时长预测仍未得到优化。本文提出了DMOSpeech 2，通过强化学习方法将度量优化扩展到时长预测器。所提出的系统实现了一种新颖的时长策略框架，使用群体相对偏好优化（GRPO），并以说话人相似度和词错误率作为奖励信号。通过优化这一先前未优化的组件，DMOSpeech 2构建了一个更完整的度量优化合成流程。此外，本文引入了教师引导采样，这是一种混合方法，在过渡到学生模型之前利用教师模型进行初始去噪步骤，在保持效率的同时显著提高了输出多样性。综合评估表明，与先前系统相比，在所有度量上均表现出优越性能，同时在不降低质量的情况下将采样步骤减少了一半。这些进展代表了朝着在多个组件上实现度量优化的语音合成系统迈出的重要一步。音频样本、代码和预训练模型可在https://dmospeech2.github.io/获取。
英文摘要：
Diffusion-based text-to-speech (TTS) systems have made remarkable progress in zero-shot speech synthesis, yet optimizing all components for perceptual metrics remains challenging. Prior work with DMOSpeech demonstrated direct metric optimization for speech generation components, but duration prediction remained unoptimized. This paper presents DMOSpeech 2, which extends metric optimization to the duration predictor through a reinforcement learning approach. The proposed system implements a novel duration policy framework using group relative preference optimization (GRPO) with speaker similarity and word error rate as reward signals. By optimizing this previously unoptimized component, DMOSpeech 2 creates a more complete metric-optimized synthesis pipeline. Additionally, this paper introduces teacher-guided sampling, a hybrid approach leveraging a teacher model for initial denoising steps before transitioning to the student model, significantly improving output diversity while maintaining efficiency. Comprehensive evaluations demonstrate superior performance across all metrics compared to previous systems, while reducing sampling steps by half without quality degradation. These advances represent a significant step toward speech synthesis systems with metric optimization across multiple components. The audio samples, code and pre-trained models are available at https://dmospeech2.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
