StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks (1806.02169v2)

Abstract: This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate converted speech signals quickly enough to allow real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic-sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.

• The paper presents a GAN-based framework that eliminates the need for parallel data by enabling many-to-many voice mappings.
• It employs a single generator network to achieve real-time voice conversion with enhanced audio quality and speaker similarity.
• Experimental results on VCC 2018 indicate that StarGAN-VC outperforms traditional methods in naturalness and conversion efficiency.

StarGAN-VC: Advancements in Non-Parallel Many-to-Many Voice Conversion

The paper under review introduces StarGAN-VC, a novel approach for non-parallel many-to-many voice conversion (VC) utilizing the StarGAN framework. This approach addresses significant limitations within existing voice transformation methodologies by introducing a system that can perform many-to-many mappings without requiring parallel data, aligning transcriptions, or specific time adjustments. By utilizing a single generative framework, StarGAN-VC offers a flexible, efficient, and scalable solution for real-time voice conversion applications.

Core Contributions

StarGAN-VC distinguishes itself through several key contributions:

1. Non-reliance on Parallel Data: A noteworthy feature of StarGAN-VC is its ability to train on non-parallel data, negating the need for extensive pre-alignment procedures that conventional methods often necessitate. This is particularly useful in scenarios where acquiring parallel utterances is impractical or infeasible.
2. Many-to-Many Voice Conversion: Leveraging the StarGAN architecture, the proposed method facilitates simultaneous learning of multiple mappings across different voice attributes within a single generator network. This contrasts with traditional one-to-one mapping methods that require separate models for each conversion pair, thereby reducing computational redundancy and enhancing efficiency.
3. Real-Time Capabilities: The paper claims the implementation allows for swift generation of voice-converted signals, potentially supporting real-time VC applications. This aspect holds practical relevance for industries requiring instantaneous voice transformation, such as telecommunications and live broadcasting.
4. Minimal Training Requirements: Demonstrating an impressive performance with only a few minutes of training examples, StarGAN-VC presents a robust framework that balances data efficiency with conversion quality, making it accessible for realistic applications where extensive training datasets are unavailable or costly to obtain.

Experimental Evaluation

The authors conducted subjective assessments focusing on non-parallel many-to-many speaker identity conversion tasks using the VCC 2018 dataset. In comparative quality tests against a VAE-GAN-based method, StarGAN-VC exhibited superior outcomes in terms of both audio quality and speaker similarity. Specifically, listeners perceived the StarGAN-VC-generated voice samples as more natural and accurately mimicking the target speaker attributes compared to the baseline.

Theoretical and Practical Implications

The theoretical implications of StarGAN-VC extend to the field of generative models in audio processing, demonstrating how GAN architectures can be adapted beyond image domain applications to tackle complex audio tasks. This underscores a significant step towards generalizing GAN capabilities across domains.

From a practical standpoint, the StarGAN-VC approach offers a scalable solution for voice conversion, particularly in applications needing versatile, quick-to-learn, and responsive systems. Its ability to handle non-parallel data effectively reduces the overhead associated with collecting and aligning extensive datasets, promising cost reductions and expanded applicability.

Future Directions

The work presents a promising trajectory for future advancements in AI-driven voice processing. Potential areas for exploration include:

• Enhancements in Real-Time Processing: Further optimization for latency reduction in real-time applications, leveraging improved hardware acceleration or network architectures.
• Expansion to Diverse Language and Accent Adaptation: Extending the framework's adaptability to broader linguistic and paralinguistic variations, potentially incorporating cross-lingual voice conversion capabilities.
• Integration with Other Speech Processing Systems: StarGAN-VC could be integrated with text-to-speech (TTS) systems or augmented reality (AR) applications, broadening its impact across emerging technology sectors.

In conclusion, the StarGAN-VC model sets a foundation for ongoing developments in voice conversion technologies, exemplifying the transformative potential of non-parallel data utilization combined with sophisticated generative adversarial networks. The insights drawn from this research could pave the way for more refined and capable audio processing solutions in the near future.