SelfTTS: cross-speaker style transfer through explicit embedding disentanglement and self-refinement using self-augmentation
Abstract
This paper presents SelfTTS, a text-to-speech (TTS) model designed for cross-speaker style transfer that eliminates the need for external pre-trained speaker or emotion encoders. The architecture achieves emotional expressivity in neutral speakers through an explicit disentanglement strategy utilizing Gradient Reversal Layers (GRL) combined with cosine similarity loss to decouple speaker and emotion information. We introduce Multi Positive Contrastive Learning (MPCL) to induce clustered representations of speaker and emotion embeddings based on their respective labels. Furthermore, SelfTTS employs a self-refinement strategy via Self-Augmentation, exploiting the model's voice conversion capabilities to enhance the naturalness of synthesized speech. Experimental results demonstrate that SelfTTS achieves superior emotional naturalness (eMOS) and robust stability in target timbre and emotion compared to state-of-the-art baselines.
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 development of SelfTTS, a robust TTS framework that achieves high-quality cross-speaker style transfer through innovative embedding disentanglement and self-refinement strategies. This work represents a meaningful advancement in the field of speech synthesis, addressing key challenges related to emotional expressivity and speaker identity while providing a solid experimental foundation to support its claims.
Methodology
The paper introduces SelfTTS, a novel TTS framework that effectively decouples speaker and emotion embeddings without relying on external encoders. The methodology employs Gradient Reversal Layers (GRL) and Multi Positive Contrastive Learning (MPCL) to achieve disentanglement and clustering of embeddings, which is a significant advancement over existing methods that often suffer from speaker leakage. The self-refinement strategy through Self-Augmentation is particularly innovative, leveraging the model’s voice conversion capabilities to enhance the naturalness of synthesized speech. This approach is well-justified and clearly articulated, demonstrating a solid understanding of the challenges in TTS.
Results & evaluation
The experimental setup is robust, utilizing both subjective (eMOS, nMOS, sMOS) and objective metrics (UTMOS, WER, SECS, EECS) to evaluate performance. The results indicate that SelfTTS outperforms state-of-the-art models in emotional naturalness and stability, which is a crucial aspect of TTS systems. The use of cross-corpus experiments adds to the credibility of the findings, although the paper could benefit from more extensive comparisons with additional baselines.
Reproducibility
The paper provides adequate implementation details, including the architecture, training procedures, and evaluation metrics, which facilitate reproducibility. The authors have made their code publicly available, enhancing the likelihood that other researchers can replicate the results. However, some hyperparameters and specific configurations could be more explicitly detailed to ensure complete clarity.
Limitations
One limitation noted is the model's performance in cross-corpus scenarios, where emotional adherence is lower due to the differences in recording conditions. Additionally, while the Self-Augmentation strategy shows promise, its effectiveness may vary based on the quality of synthetic samples generated, which could introduce artifacts into the training process.
Broader impact
The advancements presented in SelfTTS have significant implications for the development of expressive TTS systems, particularly in applications requiring emotional expressivity and speaker identity preservation. This work could benefit various fields, including virtual assistants, audiobooks, and gaming, where natural and emotionally engaging speech synthesis is essential. The main contribution of this paper is the development of SelfTTS, a robust TTS framework that achieves high-quality cross-speaker style transfer through innovative embedding disentanglement and self-refinement strategies. This work represents a meaningful advancement in the field of speech synthesis, addressing key challenges related to emotional expressivity and speaker identity while providing a solid experimental foundation to support its claims.
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