MF-Speech: Achieving Fine-Grained and Compositional Control in Speech Generation via Factor Disentanglement
Abstract
Generating expressive and controllable human speech is one of the core goals of generative artificial intelligence, but its progress has long been constrained by two fundamental challenges: the deep entanglement of speech factors and the coarse granularity of existing control mechanisms. To overcome these challenges, we have proposed a novel framework called MF-Speech, which consists of two core components: MF-SpeechEncoder and MF-SpeechGenerator. MF-SpeechEncoder acts as a factor purifier, adopting a multi-objective optimization strategy to decompose the original speech signal into highly pure and independent representations of content, timbre, and emotion. Subsequently, MF-SpeechGenerator functions as a conductor, achieving precise, composable and fine-grained control over these factors through dynamic fusion and Hierarchical Style Adaptive Normalization (HSAN). Experiments demonstrate that in the highly challenging multi-factor compositional speech generation task, MF-Speech significantly outperforms current state-of-the-art methods, achieving a lower word error rate (WER=4.67%), superior style control (SECS=0.5685, Corr=0.68), and the highest subjective evaluation scores(nMOS=3.96, sMOS_emotion=3.86, sMOS_style=3.78). Furthermore, the learned discrete factors exhibit strong transferability, demonstrating their significant potential as a general-purpose speech representation.
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 MF-Speech, a framework that effectively disentangles speech factors and achieves fine-grained, compositional control in speech generation. This work significantly advances the state-of-the-art in controllable speech synthesis, addressing key challenges in the field and providing a robust foundation for future research and applications.
Methodology
The proposed MF-Speech framework presents a novel approach to disentangling speech factors—content, timbre, and emotion—through a multi-objective optimization strategy. The architecture of MF-SpeechEncoder and MF-SpeechGenerator is well-structured, with specific modules designed for each factor, enhancing the purity and independence of representations. The use of dynamic fusion and Hierarchical Style Adaptive Normalization (HSAN) for fine-grained control is innovative, allowing for a more nuanced synthesis of speech that can adaptively modulate style and content. However, the complexity of the model may pose challenges in terms of implementation and understanding.
Results & evaluation
The experiments conducted are comprehensive, comparing MF-Speech against multiple state-of-the-art methods across various metrics. The results show significant improvements in word error rate (WER), style control, and subjective evaluation scores, indicating the effectiveness of the proposed framework. The use of both objective and subjective evaluation methods strengthens the reliability of the findings. However, the paper could benefit from additional details on the datasets used and the specific configurations for the experiments.
Reproducibility
The paper provides a reasonable level of detail regarding the architecture and training process of MF-Speech. However, it lacks specific implementation details such as hyperparameter settings and the exact training procedure, which could hinder reproducibility. The inclusion of a demo URL is a positive aspect, as it allows for practical evaluation of the model's capabilities.
Limitations
One limitation is the potential for overfitting due to the complexity of the model and the reliance on a specific dataset (ESD dataset). Additionally, while the framework shows promise in generating expressive speech, the generalization to diverse speakers and emotions remains to be fully validated. The subjective evaluation, while valuable, is based on a limited number of participants, which may not capture the full range of user experiences.
Broader impact
The advancements presented in MF-Speech have significant implications for applications in voice synthesis, personalized digital assistants, and media production. By enabling fine-grained control over speech attributes, the framework can enhance user interaction and experience in various domains, including entertainment, accessibility, and education. The potential for transferability of the learned factors also opens avenues for further research and application in related fields. The main contribution of this paper is the introduction of MF-Speech, a framework that effectively disentangles speech factors and achieves fine-grained, compositional control in speech generation. This work significantly advances the state-of-the-art in controllable speech synthesis, addressing key challenges in the field and providing a robust foundation for future research and applications.
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