SALAD: Exploring Continuous Representation in Zero-Shot Text-to-Speech Synthesis using Per-Token Latent Diffusion

Alapfogalmak

Quantizing inherently continuous modalities like audio for text-to-speech synthesis may be suboptimal, and continuous representation learning using per-token latent diffusion models like SALAD offers a competitive alternative with superior intelligibility.

Kivonat

Bibliographic Information:

Turetzky, A., Shabtay, N., Shechtman, S., Haws, D., Aronowitz, H., Hoory, R., & Dekel, A. (2024). Continuous Speech Synthesis using per-token Latent Diffusion. arXiv preprint arXiv:2410.16048.

Research Objective:

This paper investigates the effectiveness of continuous representation learning, specifically using per-token latent diffusion, for zero-shot text-to-speech (TTS) synthesis compared to traditional discrete representation methods.

Methodology:

The authors propose SALAD, a novel per-token latent diffusion model for zero-shot TTS. They develop three variants of SALAD: Text2Acoustic (T2A), Semantic2Acoustic Autoregressive (S2A-AR), and Semantic2Acoustic Non-Autoregressive (S2A-NAR). For each variant, they train comparable models using discrete representations based on Residual Vector Quantization (RVQ) for comparison. The models are trained on the English subset of the multi-lingual LibriSpeech dataset and evaluated on LibriSpeech test-clean using objective metrics (UTMOS, CER, speaker similarity) and subjective listening tests (MOS for speech quality and naturalness, similarity score for speaker similarity).

Key Findings:

• Both continuous and discrete representation models demonstrate strong performance in zero-shot TTS.
• SALAD's T2A model achieves the highest intelligibility score among all models tested.
• Subjective listening tests reveal that the T2A continuous and S2A-NAR discrete models achieve speech quality and speaker similarity comparable to ground truth audio.
• Increasing the fidelity of representations (higher VAE embedding dimension or more RVQ codebooks) leads to a more significant drop in intelligibility for the discrete model compared to the continuous model.
• VAE sampling during training contributes to the robustness of the continuous model.

Main Conclusions:

The study suggests that continuous representation learning using per-token latent diffusion is a viable and competitive approach for zero-shot TTS, potentially outperforming traditional discrete methods in terms of intelligibility while maintaining comparable quality.

Significance:

This research contributes to the advancement of TTS technology by exploring the potential of continuous representation learning, paving the way for more natural and intelligible synthetic speech.

Limitations and Future Research:

The diffusion head inference process is slower than RVQ prediction heads. Future research could focus on optimizing the inference speed of diffusion-based models and developing quality metrics for diffusion processes to enable advanced decoding algorithms. Further exploration of multimodal models operating on symmetric representations for both perception and generation is also suggested.

Statisztikák

The continuous T2A model achieves a UTMOS score of 4.280 and a CER of 0.739%.
The discrete T2A model achieves a UTMOS score of 4.270 and a CER of 2.298%.
The continuous S2A-NAR model achieves a UTMOS score of 4.277 and a CER of 1.393%.
The discrete S2A-NAR model achieves a UTMOS score of 4.351 and a CER of 1.846%.
The high-fidelity continuous T2A model (d=32) achieves a CER of 1.157%.
The high-fidelity discrete T2A model (Q=12) achieves a CER of 5.461%.

Idézetek

"We therefore suspect that quantizing inherently continuous modalities may be sub-optimal, and focus on continuous alternatives instead."
"Our results demonstrate that both continuous and discrete approaches are highly competent, and that SALAD achieves a superior intelligibility score while obtaining speech quality and speaker similarity on par with the ground-truth audio."

Főbb Kivonatok

Continuous Speech Synthesis using per-token Latent Diffusion

by Arnon Turetz... : arxiv.org 10-22-2024

https://arxiv.org/pdf/2410.16048.pdf

Mélyebb kérdések

How might the advancements in continuous representation learning for TTS impact other audio-related tasks, such as speech recognition or music generation?

Answer: Advancements in continuous representation learning for TTS, as exemplified by SALAD and its use of per-token latent diffusion, hold significant potential to revolutionize other audio-related tasks like speech recognition and music generation. Here's how:
Speech Recognition:

Improved Acoustic Modeling: Continuous representations capture the nuances of speech signals more effectively than discrete tokens, potentially leading to more accurate acoustic models in speech recognition systems. This could translate to lower word error rates, especially in noisy environments or with accented speech.
Robustness to Noise: Continuous models, by their nature, are more adept at handling noise in the input signal. This inherent robustness could lead to speech recognition systems that are less susceptible to degradation in real-world scenarios with background noise.
Unified Architectures: The success of continuous representations in TTS could motivate the development of unified architectures for speech recognition and synthesis. Sharing components and representations across these tasks could lead to more efficient and data-efficient models.
Music Generation:

Expressiveness and Nuance: Continuous representations have the potential to capture the subtle variations in pitch, timbre, and timing that characterize expressive music. This could lead to music generation models that produce more realistic, emotionally resonant, and less "robotic" sounding music.
Fine-Grained Control: Working in a continuous space might offer finer control over the generated audio, allowing musicians and composers to manipulate specific aspects of the music with greater precision.
Cross-Modal Generation: Continuous representations could facilitate more seamless cross-modal generation tasks, such as generating music from text prompts or images. This could open up new creative avenues for artists and composers.
Challenges:

Computational Cost: Continuous models, especially those based on diffusion processes, can be computationally expensive to train and sample from. Overcoming these computational challenges is crucial for wider adoption.
Evaluation Metrics: Evaluating the quality of continuous representations for tasks like speech recognition and music generation requires developing new metrics that go beyond traditional measures.

Could the limitations of discrete representations be mitigated by exploring alternative quantization techniques or model architectures, potentially closing the performance gap with continuous models?

Answer: Yes, the limitations of discrete representations in audio modeling can be partially mitigated by exploring alternative quantization techniques and model architectures, potentially narrowing the performance gap with continuous models. Here are some promising avenues:
Quantization Techniques:

Vector Quantization with Larger Codebooks: Increasing the size of the codebooks in Vector Quantized Variational Autoencoders (VQ-VAEs) can lead to finer-grained representations and reduce quantization error. However, this comes at the cost of increased computational complexity.
Learned Quantization: Instead of using fixed codebooks, learned quantization techniques allow the model to adapt the quantization process during training, potentially leading to more efficient and perceptually relevant representations.
Hierarchical Quantization: Employing multiple levels of quantization, with coarser representations at higher levels and finer representations at lower levels, can capture a wider range of acoustic information.
Model Architectures:

Improved Conditioning Mechanisms: Developing more sophisticated mechanisms for conditioning the generation of discrete tokens on contextual information, such as text or speaker embeddings, can enhance the coherence and naturalness of the synthesized audio.
Hybrid Discrete-Continuous Models: Exploring hybrid architectures that combine the strengths of both discrete and continuous representations could offer a good balance between expressiveness and computational efficiency.
Closing the Gap:
While these advancements can improve the performance of discrete models, completely closing the gap with continuous models might be challenging. Continuous representations inherently offer a higher degree of freedom and nuance, which might be difficult to fully replicate with discrete tokens.

If we can successfully model and generate complex sensory experiences like audio through continuous representations, what does this imply about the nature of human perception and our ability to create artificial intelligence that can truly understand and interact with the world?

Answer: The ability to model and generate complex sensory experiences like audio through continuous representations has profound implications for our understanding of human perception and the pursuit of artificial intelligence that can genuinely comprehend and engage with the world:
Nature of Human Perception:

Continuous Representation in the Brain: The success of continuous models suggests that the brain might also rely on continuous representations to process and interpret sensory information. This challenges the traditional view of the brain as a purely symbolic processor.
Subtlety and Nuance: Our ability to perceive and appreciate the subtle nuances in audio, such as emotional inflection in speech or the timbre of a musical instrument, points to the importance of continuous representations in capturing the richness of sensory experience.
Artificial Intelligence:

More Human-Like AI: AI systems that can model and generate sensory experiences using continuous representations have the potential to interact with the world in a more human-like manner. This could lead to more natural and engaging human-computer interfaces.
Deeper Understanding: The ability to generate realistic sensory experiences suggests a deeper level of understanding of the underlying data and the perceptual mechanisms involved. This could pave the way for AI systems that can reason about and manipulate sensory information in more sophisticated ways.
Ethical Considerations: As AI systems become increasingly adept at generating realistic sensory experiences, it raises ethical concerns about potential misuse, such as the creation of deepfakes or the manipulation of human emotions.
Conclusion:
The success of continuous representations in modeling sensory experiences represents a significant step towards creating AI that can truly understand and interact with the world in a way that is more aligned with human perception. However, it also underscores the importance of addressing the ethical implications of these advancements.