Semantic-VAE: Semantic-Alignment Latent Representation for Better Speech Synthesis
Abstract
While mel-spectrograms have been widely utilized as intermediate representations in zero-shot text-to-speech (TTS), their inherent redundancy leads to inefficiency in learning text-speech alignment. Compact VAE-based latent representations have recently emerged as a stronger alternative, but they also face a fundamental optimization dilemma: higher-dimensional latent spaces improve reconstruction quality and speaker similarity, but degrade intelligibility, while lower-dimensional spaces improve intelligibility at the expense of reconstruction fidelity. To overcome this dilemma, we propose Semantic-VAE, a novel VAE framework that utilizes semantic alignment regularization in the latent space. This design alleviates the reconstruction-generation trade-off by capturing semantic structure in high-dimensional latent representations. Extensive experiments demonstrate that Semantic-VAE significantly improves synthesis quality and training efficiency. When integrated into F5-TTS, our method achieves 2.10% WER and 0.64 speaker similarity on LibriSpeech-PC, outperforming mel-based systems (2.23%, 0.60) and vanilla acoustic VAE baselines (2.65%, 0.59). We also release the code and models to facilitate further research.
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 paper presents Semantic-VAE, a novel VAE framework that improves speech synthesis by addressing the reconstruction-generation trade-off through semantic alignment regularization. This work represents a meaningful advancement in TTS technology, combining innovative methodological contributions with strong empirical validation, thereby holding promise for future applications and research in the field.
Methodology
The proposed Semantic-VAE framework introduces a novel approach to address the optimization dilemma in high-dimensional latent spaces by incorporating semantic alignment regularization. This method effectively captures semantic structures while maintaining reconstruction fidelity, showcasing a well-thought-out integration of existing VAE architectures with innovative regularization techniques. The methodology is clearly articulated, with a comprehensive explanation of the training objectives and loss functions, making it a significant advancement in the field of TTS.
Results & evaluation
The experiments are robust, utilizing extensive datasets such as LibriTTS and Libriheavy, and evaluating both reconstruction and downstream TTS performance. The results demonstrate clear improvements over baseline models, with detailed metrics provided for both intelligibility and speaker similarity. The ablation studies further substantiate the contributions of each component, enhancing the credibility of the findings.
Reproducibility
The paper provides sufficient implementation details, including model architecture, training setup, and hyperparameters, which facilitate reproducibility. The release of code and models on GitHub is a positive aspect, promoting further research and validation of the results.
Limitations
While the paper addresses the optimization dilemma effectively, it does not explore the potential trade-offs in computational efficiency or the scalability of the approach to larger datasets or more complex TTS tasks. Additionally, the reliance on pre-trained SSL models may limit the generalizability of the findings across different languages or dialects.
Broader impact
The Semantic-VAE framework has significant implications for the development of more efficient and high-quality TTS systems, potentially enhancing applications in voice synthesis, accessibility technologies, and personalized speech interfaces. Its contributions could pave the way for future research on advanced alignment techniques in multimodal machine learning. The paper presents Semantic-VAE, a novel VAE framework that improves speech synthesis by addressing the reconstruction-generation trade-off through semantic alignment regularization. This work represents a meaningful advancement in TTS technology, combining innovative methodological contributions with strong empirical validation, thereby holding promise for future applications and research in the field.
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