Direct Preference Optimization for Speech Autoregressive Diffusion Models
Abstract
Autoregressive diffusion models (ARDMs) have recently been applied to speech generation, achieving state-of-the-art (SOTA) performance in zero-shot text-to-speech. By autoregressively generating continuous speech tokens with next-token diffusion, these models offer a promising alternative to next-token prediction, avoiding the technical complexities associated with discrete speech tokenization. As a relatively new paradigm, research on reinforcement learning (RL)-based fine-tuning of speech ARDMs remains limited. In this paper, we propose Autoregressive Diffusion-Direct Preference Optimization (ARDM-DPO) to advance this research. By fine-tuning the recently proposed zero-shot text-to-speech model DiTAR with DPO, we achieve significant improvements in terms of speech expressiveness and robustness for long texts.
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 main contribution of this paper is the introduction of ARDM-DPO, a novel method for fine-tuning autoregressive diffusion models in speech generation, which enhances expressiveness and robustness while addressing the challenges of traditional TTS systems. The comprehensive evaluation of the method demonstrates its potential impact on the field of audio generation and reinforces the importance of preference alignment in machine learning models.
Methodology
The proposed method, Autoregressive Diffusion-Direct Preference Optimization (ARDM-DPO), represents a significant advancement in the application of autoregressive diffusion models for text-to-speech (TTS) systems. The methodology effectively integrates reinforcement learning principles to fine-tune the DiTAR model, addressing the limitations of traditional next-token prediction approaches. The authors provide a clear framework for preference alignment, which is critical for enhancing the expressiveness and robustness of generated speech. However, the paper could benefit from a more detailed discussion on the implementation specifics of DPO in the context of ARDMs, as well as a deeper exploration of the underlying assumptions made during model training.
Results & evaluation
The experiments are well-structured, utilizing comprehensive datasets and benchmarks to evaluate the performance of ARDM-DPO against baseline methods. The authors present quantitative metrics such as F0 variance and character error rate, alongside qualitative assessments through listener evaluations, which provide a balanced view of the model's performance. The results indicate significant improvements in expressiveness and robustness, although the paper notes some instability in training, which warrants further investigation. The use of a large preference dataset strengthens the findings, but additional comparisons with more baseline models could enhance the robustness of the conclusions drawn.
Reproducibility
The paper provides a reasonable level of detail regarding the experimental setup, including model architecture, training parameters, and evaluation metrics. However, the absence of a publicly available code repository or demo limits the reproducibility of the results. Future work should consider releasing the model and code to facilitate further research and validation by the community.
Limitations
The paper acknowledges the instability of the ARDM-DPO training process, particularly in Task A, which can lead to degradation in speech quality. This instability raises questions about the robustness of the method in practical applications. Additionally, the reliance on preference datasets for training may introduce biases that affect the generalizability of the model. The authors also mention the need for early stopping, which could complicate the training process.
Broader impact
The advancements presented in this paper have the potential to significantly improve TTS systems, making them more expressive and aligned with human preferences. This could enhance applications in various fields, including virtual assistants, audiobooks, and entertainment. The work contributes to the growing body of research on autoregressive diffusion models, potentially influencing future developments in multimodal generation tasks. The main contribution of this paper is the introduction of ARDM-DPO, a novel method for fine-tuning autoregressive diffusion models in speech generation, which enhances expressiveness and robustness while addressing the challenges of traditional TTS systems. The comprehensive evaluation of the method demonstrates its potential impact on the field of audio generation and reinforces the importance of preference alignment in machine learning models.
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