Diffusion-based Expressive Voice Conversion: Enabling Any-to-Any Conversion with Emotional and Speaker Representations

Core Concepts

A fully end-to-end expressive voice conversion framework based on a conditional diffusion model that effectively models both speaker-dependent emotional cues and speaker-independent emotional style to enable any-to-any conversion.

Abstract

The paper proposes a novel framework for expressive voice conversion called DEVC, which utilizes a conditional denoising diffusion probabilistic model (DDPM) to enable end-to-end conversion without the need for vocoders.
Key highlights:

DEVC employs three encoders to extract content representations, speaker representations with emotional cues, and speaker-independent emotion representations, respectively.
The speaker representations derived from a pre-trained speaker verification model are found to effectively capture speaker-dependent emotional information, which is crucial for expressive voice conversion.
The proposed framework demonstrates flexibility in converting both seen and unseen speakers, achieving superior performance in objective and subjective evaluations compared to baseline methods.
DEVC is the first end-to-end diffusion model-based expressive voice conversion framework known to the authors.

Stats

Mel-cepstral distortion (MCD) values for DEVC are consistently lower than the baselines, indicating better quality and closer similarity between the synthesized and target samples.
DEVC achieves higher speaker verification (SV) accuracy compared to the baselines, demonstrating its capability to accurately convert speaker identity.
DEVC outperforms the baselines in terms of voicing decision error (VDE) and F0 frame error (FFE), indicating its effectiveness in capturing and reproducing the desired emotional characteristics.

Quotes

"A fully end-to-end expressive voice conversion framework based on a conditional diffusion model without the need for large-scale training data and manual annotations."
"Our findings reveal that speaker embeddings derived from an SV model pre-trained on neutral data effectively capture speaker-dependent emotional cues, thereby demonstrating their utility in enhancing expressive voice conversion."
"Our proposed framework shows flexibility in identity conversion for both seen and unseen emotional speakers, achieving any-to-any expressive voice conversion."

Key Insights Distilled From

Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model

by Zongyang Du,... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01730.pdf

Deeper Inquiries

How can the proposed DEVC framework be extended to enable conversion of emotion states for the same speaker (emotional voice conversion) or different speakers

To extend the DEVC framework for emotional voice conversion for the same speaker or different speakers, several modifications and additions can be implemented. Firstly, the emotion encoder can be enhanced to capture a wider range of emotional states and nuances specific to each speaker. This can involve training the emotion encoder on a more diverse emotional speech dataset to improve its ability to recognize and represent various emotional expressions accurately. Additionally, incorporating a feedback mechanism that allows for real-time adjustment of emotional style during conversion can further enhance the emotional voice conversion process. By enabling the model to adapt and refine emotional features based on user feedback, the emotional voice conversion can be personalized and optimized for individual speakers.

What are the potential limitations of the current DEVC framework, and how can it be further improved to handle more diverse emotional expressions and speaker characteristics

While the DEVC framework shows promising results in expressive voice conversion, there are potential limitations that can be addressed for further improvement. One limitation is the generalization of emotional style representations across different speakers, especially in handling extreme or rare emotional expressions. To overcome this limitation, the model can be trained on a more extensive emotional speech dataset that includes a diverse range of emotional states and expressions. Additionally, incorporating multi-modal features such as facial expressions or physiological signals can provide additional cues for more accurate emotional style modeling. Furthermore, integrating reinforcement learning techniques to optimize the emotional voice conversion process based on user feedback can enhance the adaptability and performance of the framework in handling diverse emotional expressions and speaker characteristics.

Given the success of DEVC in expressive voice conversion, how can the insights and techniques from this work be applied to other speech synthesis tasks, such as text-to-speech or speech enhancement, to improve their expressiveness and naturalness

The success of the DEVC framework in expressive voice conversion can be leveraged to enhance other speech synthesis tasks such as text-to-speech or speech enhancement. By incorporating the insights and techniques from DEVC, these tasks can benefit from improved expressiveness and naturalness in synthesized speech. For text-to-speech systems, integrating emotional style modeling based on speaker-dependent and speaker-independent emotional features can enable the generation of more emotionally expressive speech. This can enhance the user experience in applications like virtual assistants or audiobooks by providing more engaging and personalized interactions. In speech enhancement tasks, the techniques used in DEVC for disentangling speaker identity and emotional style can be applied to improve the quality and clarity of speech signals, especially in noisy or degraded audio environments. By incorporating emotional style transfer capabilities, speech enhancement systems can preserve and enhance the emotional content of speech, leading to more natural and engaging audio output.