Efficient Voice Style Conversion with Hybrid Text and Audio Prompts

Kernkonzepte

HybridVC is an efficient any-to-any voice conversion model that supports both text and audio prompts to achieve flexible voice style conversion.

Zusammenfassung

HybridVC is a voice conversion (VC) framework built upon a pre-trained conditional variational autoencoder (CVAE) that combines the strengths of a latent model with contrastive learning. It supports both text and audio prompts, enabling more flexible voice style conversion compared to previous methods that rely solely on text or audio prompts.

Key highlights:

• HybridVC models a latent distribution conditioned on speaker embeddings acquired by a pre-trained speaker encoder. It optimizes style text embeddings to align with the speaker style information through contrastive learning in parallel.
• This approach enables HybridVC to be efficiently trained under limited computational resources, while maintaining competitive performance on speech intelligibility, naturalness, and audio quality compared to baseline models.
• Experiments demonstrate HybridVC's superior training efficiency and its capability for advanced multimodal voice style conversion, underscoring its potential for widespread applications such as user-defined personalized voice in various social media platforms.
• A comprehensive ablation study further validates the effectiveness of the proposed method, including the benefits of the latent model and the negative sampling technique for text embedding optimization.

Statistiken

HybridVC can achieve competitive performance on speech intelligibility, naturalness, and audio quality with only 15 hours of training on limited computational resources.
HybridVC maintains overall performance without noticeable degradation when extending training to the PromptSpeech dataset, despite the backbone CVAE only being pre-trained on the VCTK training set.
HybridVC effectively maintains the prosody of source speech and audio quality, and accurately converts the voice characteristics given audio and text prompts.
HybridVC successfully adapts voices to match the specified style text prompts, with an accuracy of 89.8% for "higher pitch" and 91.1% for "higher volume" prompts.

Zitate

"HybridVC supports text and audio prompts, enabling more flexible voice style conversion."
"HybridVC can be efficiently trained under limited computational resources and achieves a flexible VC system that supports hybrid prompts."
"HybridVC supports small-scale training which can be easily adapted to applications such as user-defined personalised voice."

Wichtige Erkenntnisse aus

HybridVC: Efficient Voice Style Conversion with Text and Audio Prompts

by Xinlei Niu,J... um arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15637.pdf

Tiefere Fragen

How can HybridVC be further extended to support more diverse and complex voice style prompts, beyond the single-factor text prompts explored in the current study

HybridVC can be extended to support more diverse and complex voice style prompts by incorporating advanced natural language processing techniques and leveraging larger and more diverse datasets. One approach could involve integrating transformer-based models like GPT-3 or BERT to enhance the text understanding capabilities of the system. These models can process more complex and nuanced text prompts, allowing for a wider range of voice style variations to be captured. Additionally, incorporating multi-modal learning techniques that combine text, audio, and image data could further enrich the prompt understanding and enable HybridVC to generate more diverse and personalized voice styles. By training the model on a more extensive dataset that includes a broader spectrum of voice styles and prompts, HybridVC can learn to adapt to a wider range of inputs and produce more accurate and natural-sounding voice conversions.

What are the potential limitations of the text-speaker embedding alignment in HybridVC, and how could techniques like prompt tuning be leveraged to improve the model's sensitivity to a wider range of text prompts

One potential limitation of the text-speaker embedding alignment in HybridVC is its sensitivity to certain types of text prompts, particularly those that include ambiguous or less distinct style descriptors. To address this limitation, techniques like prompt tuning can be leveraged to improve the model's sensitivity and alignment to a wider range of text prompts. Prompt tuning involves fine-tuning the model on a specific set of prompts to enhance its ability to capture the nuances and variations in voice styles associated with those prompts. By training HybridVC on a diverse set of annotated text prompts and speaker embeddings, the model can learn to better align text descriptions with corresponding voice styles, improving the overall accuracy and consistency of voice conversions. Additionally, incorporating reinforcement learning techniques to provide feedback and guidance on the alignment of text and speaker embeddings could further enhance the model's sensitivity to a broader range of text prompts.

Given the efficiency and flexibility of HybridVC, how could it be integrated into real-world applications, such as personalized voice assistants or social media platforms, to enhance user experiences

Given the efficiency and flexibility of HybridVC, it can be seamlessly integrated into real-world applications such as personalized voice assistants or social media platforms to enhance user experiences. In personalized voice assistants, HybridVC can be used to customize the voice of the assistant based on user preferences, creating a more engaging and personalized interaction. By allowing users to choose from a variety of voice styles and prompts, HybridVC can tailor the assistant's voice to suit individual preferences, making the interaction more enjoyable and relatable. In social media platforms, HybridVC can enable users to create unique and personalized voice content for their posts, messages, or videos. This can enhance user engagement, creativity, and self-expression on social media, leading to a more dynamic and interactive user experience. Additionally, HybridVC can be integrated into chatbots, virtual reality applications, and other interactive systems to provide a more natural and engaging user interface, enhancing the overall user experience and satisfaction.