SelfVC: Iterative Voice Conversion with Self-Synthesized Transformations

Core Concepts

SelfVC is a training strategy that utilizes self-generated voice transformations to iteratively improve a controllable voice conversion model on imperfectly disentangled speech representations.

Abstract

The paper proposes SelfVC, a framework for text-free voice conversion that can modify the voice of a given utterance while providing control over the prosody of the synthesized speech. The key components are: Feature Extraction: The framework derives content (linguistic) embedding, speaker embedding, pitch contour, and duration information from the input audio signal. Synthesizer Model: The synthesizer reconstructs the mel-spectrogram from the extracted features, with intermediate modules to predict the pitch and duration. Iterative Refinement using Self Transformations: To train an effective voice conversion model, the authors propose a strategy that utilizes the synthesizer itself to generate diverse voice-converted transformations of the input audio. At each training iteration, the current state of the synthesizer is used to transform the content embedding, and the model is updated to reconstruct the original audio from this self-transformed input. The authors demonstrate that incorporating self-synthesized transformations during training leads to significant improvements in speaker similarity metrics compared to using only heuristic transformations. SelfVC achieves state-of-the-art results in zero-shot voice conversion for English and outperforms prior approaches in cross-lingual voice conversion tasks.

Stats

The Conformer-SSL model used as the content encoder is pretrained on 56k hours of unlabelled English speech from the LibriLight dataset. The mel-spectrogram synthesizer and HiFiGAN vocoder are trained on the train-clean-360 subset of the LibriTTS dataset, which contains 360 hours of speech from 904 speakers.

Quotes

"To expand on the heuristic set of transforms, we propose to utilize the synthesizer model itself to generate a voice-converted variation of a given utterance." "SelfVC achieves SOTA results in zero-shot voice conversion for English and can be easily scaled to multiple languages in a text-free manner, outperforming prior approaches in cross-lingual voice conversion."

Key Insights Distilled From

SelfVC: Voice Conversion With Iterative Refinement using Self Transformations

by Paarth Neekh... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2310.09653.pdf

Deeper Inquiries

How can the self-transformation strategy be extended to other generative tasks beyond voice conversion, such as text-to-speech or image synthesis

The self-transformation strategy employed in SelfVC for voice conversion can be extended to other generative tasks like text-to-speech or image synthesis by adapting the training framework to suit the specific characteristics of the new tasks. For text-to-speech synthesis, the self-transformation approach can involve perturbing the linguistic content representations derived from text inputs to generate a diverse set of training examples. This can help the model learn to generate speech with varying styles, accents, or emotions. Similarly, in image synthesis tasks, the self-transformation strategy can involve manipulating the latent representations of images to create novel variations or styles in the generated images. By iteratively refining the model with self-generated examples, the model can learn to capture the underlying structure and characteristics of the data domain, leading to improved generative performance across different tasks.

What are the potential limitations or drawbacks of the self-transformation approach, and how can they be addressed

While the self-transformation approach in SelfVC offers several advantages in training controllable synthesis models, there are potential limitations and drawbacks that need to be considered. One limitation is the risk of overfitting to the self-transformed examples, which may result in the model being less robust to unseen data during inference. To address this, techniques such as regularization methods like dropout or weight decay can be employed to prevent overfitting and improve generalization. Another drawback is the computational cost associated with generating self-transformed examples during training, which can increase training time and resource requirements. This can be mitigated by optimizing the self-transformation process and leveraging parallel computing resources for efficient training. Additionally, the effectiveness of the self-transformation strategy may vary depending on the complexity and diversity of the data distribution, requiring careful tuning of hyperparameters and training procedures to achieve optimal results.

How can the prosody modeling capabilities of SelfVC be leveraged for applications like expressive speech synthesis or emotional voice conversion

The prosody modeling capabilities of SelfVC can be leveraged for applications like expressive speech synthesis or emotional voice conversion by incorporating additional features or cues related to prosody in the training process. For expressive speech synthesis, the model can be trained to capture and reproduce specific prosodic elements such as intonation, rhythm, and emphasis to convey different emotions or tones in the synthesized speech. By integrating prosody modeling techniques with emotional voice conversion, the model can learn to adapt the prosodic features of the input speech to reflect the desired emotional content in the output. This can enhance the naturalness and expressiveness of the synthesized speech, making it more engaging and emotionally resonant for various applications in human-computer interaction, virtual assistants, or entertainment.