Unsupervised Discovery of Latent Speaker Manifold for Speech Synthesis: VoxGenesis

Core Concepts

VoxGenesis introduces an unsupervised speech synthesis framework that discovers a latent speaker manifold and enables voice editing without supervision. By transforming a Gaussian distribution into speech distributions conditioned by semantic tokens, VoxGenesis disentangles speaker characteristics from content information.

Abstract

VoxGenesis proposes an unsupervised approach to speech synthesis, allowing for the discovery of latent speaker characteristics. The model enables voice editing by manipulating latent codes along identified directions associated with specific speaker attributes such as gender, pitch, tone, and emotion. Through extensive experiments, VoxGenesis demonstrates superior performance in producing diverse and realistic speakers compared to previous approaches.
The paper discusses the limitations of current speech synthesis models in generating new voices and highlights the importance of disentangling content from speaker features. VoxGenesis's innovative approach transforms a Gaussian distribution into speech distributions conditioned on semantic tokens, enabling more sophisticated voice editing and customization. The model's ability to uncover human-interpretable directions associated with specific speaker characteristics sets it apart from traditional supervised methods.
VoxGenesis leverages deep generative models to transform random noise into meaningful speech distributions while maintaining control over semantic information. By integrating a mapping network, shared embedding layer, and semantic transformation matrices, VoxGenesis can identify major variances in the latent space associated with different speaker attributes. The model's unique architecture allows for efficient encoding of external speaker representations and stable training processes.
The evaluation results showcase VoxGenesis's effectiveness in generating diverse and realistic speakers with distinct characteristics. The model outperforms previous approaches in terms of fidelity to training speakers, diversity in generated speakers, and overall speech quality. Additionally, VoxGenesis demonstrates promising results in zero-shot voice conversion tasks and multi-speaker TTS applications.

Stats

Achieving nuanced and accurate emulation of human voice has been a longstanding goal in artificial intelligence.
Mainstream speech synthesis models rely on supervised speaker modeling and explicit reference utterances.
In this paper, we propose VoxGenesis, an unsupervised speech synthesis framework.
VoxGenesis transforms a Gaussian distribution into speech distributions conditioned by semantic tokens.
Sampling from the Gaussian distribution enables the creation of novel speakers with distinct characteristics.
Extensive experiments show that VoxGenesis produces significantly more diverse and realistic speakers than previous approaches.
Latent space manipulation uncovers human-interpretable directions associated with specific speaker characteristics.
Voice editing is enabled by manipulating latent codes along identified directions.
VoxGenesis can be used in voice conversion and multi-speaker TTS applications.

Quotes

"VoxGenesis introduces an unsupervised approach to speech synthesis."
"The model enables voice editing by manipulating latent codes along identified directions."

Key Insights Distilled From

VoxGenesis

by Weiwei Lin,C... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00529.pdf

Deeper Inquiries

How does the use of unsupervised learning impact the scalability and generalizability of speech synthesis models

The use of unsupervised learning in speech synthesis models, as exemplified by VoxGenesis, has significant implications for scalability and generalizability.
Scalability:

Reduced Dependency on Labeled Data: Unsupervised learning eliminates the need for manually labeled data, making it easier to scale up training datasets without the labor-intensive process of annotation.
Broader Speaker Coverage: By not relying on specific speaker labels during training, unsupervised models like VoxGenesis can potentially capture a wider range of speaker characteristics and nuances. This scalability allows for more diverse and inclusive voice generation.
Generalizability:

Novel Speaker Generation: Unsupervised models excel at generating new voices that were not present in the training dataset. This ability enhances generalizability by creating unique speakers with distinct characteristics beyond those seen during training.
Interpretable Latent Space: The latent space learned through unsupervised methods like VoxGenesis enables interpretable directions associated with specific speaker attributes such as gender, pitch, tone, and emotion. This interpretability enhances the model's ability to generalize across different speakers while maintaining control over various voice characteristics.
In essence, unsupervised learning promotes scalability by reducing data labeling requirements and enhances generalizability by enabling novel speaker generation and interpretable latent spaces.

What are the potential ethical implications of using AI-generated voices for various applications

The utilization of AI-generated voices raises several ethical considerations across various applications:
Privacy Concerns:

Voice Cloning: AI-generated voices could be misused for impersonation or fraud purposes if used maliciously to clone someone's voice without consent.
Misinformation & Manipulation:

Deepfakes: AI-generated voices can contribute to the spread of misinformation through manipulated audio content that may deceive individuals or manipulate public opinion.
Representation & Bias:

Underrepresented Voices: There is a risk that certain demographics or accents might be underrepresented in synthesized voices due to biases in training data or algorithms.
Consent & Ownership:

Ownership Rights: Issues regarding who owns the rights to synthesized voices may arise when using them commercially or in creative works without clear guidelines on ownership.
To address these ethical concerns effectively, regulations around voice cloning technologies must be established to protect privacy rights and prevent misuse. Transparency about the use of AI-generated voices should also be emphasized to mitigate potential risks related to misinformation.

How might advancements in unsupervised techniques like VoxGenesis influence future developments in natural language processing

Advancements in unsupervised techniques like VoxGenesis have profound implications for future developments in natural language processing (NLP):
Enhanced Voice Synthesis Capabilities:

Models like VoxGenesis enable more nuanced control over generated speech attributes such as emotion, intonation, pitch, and speaking style. These advancements could lead to more realistic synthetic voices across various applications.
Improved Multimodal Integration:

Integrating advanced speech synthesis capabilities from models like VoxGenesis with other modalities such as text-to-speech systems can enhance multimodal communication interfaces' effectiveness.
Ethical Considerations & Regulation:

As AI-generated content becomes increasingly sophisticated with tools like VoxGenesis,
there will be a growing need for robust ethical guidelines surrounding its usage,
particularly concerning deepfake detection technology implementation within NLP
frameworks.
These advancements pave the way for more personalized user experiences through tailored synthetic speech while necessitating careful consideration of ethical implications surrounding their deployment.