DMOSpeech 2: Reinforcement Learning for Duration Prediction in
  Metric-Optimized Speech Synthesis
Authors: name: Yinghao Aaron Li, name: Xilin Jiang, name: Fei Tao, name: Cheng Niu, name: Kaifeng Xu, name: Juntong Song, name: Nima Mesgarani

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/.