Report on Current Developments in Text-to-Speech Synthesis
General Direction of the Field
The field of Text-to-Speech (TTS) synthesis is witnessing a significant shift towards more efficient, high-quality, and versatile systems. Recent advancements are focusing on reducing the dependency on large datasets, improving the computational efficiency of models, and enhancing the naturalness and diversity of synthesized speech. Innovations in diffusion models, articulatory synthesis, and universal vocoders are leading the way in making TTS systems more accessible and adaptable to various applications.
Efficiency and Data Dependency: There is a notable trend towards developing models that can achieve state-of-the-art performance with significantly less training data. This is crucial for reducing the computational burden and making TTS more practical for real-world applications. Techniques such as latent diffusion and parameter-efficient architectures are being explored to achieve this goal.
Quality and Naturalness: The pursuit of high-quality, natural-sounding speech continues to be a central focus. Recent approaches are incorporating differentiable digital signal processing (DDSP) and adversarial training to improve the synthesis quality. Additionally, methods for mitigating the training-inference mismatch are being developed to enhance the naturalness of synthetic speech.
Versatility and Personalization: The ability to generate diverse and personalized speech is becoming increasingly important. Frameworks that leverage large language models (LLMs) for synthetic conversation generation and foundation models for industry-level applications are emerging. These systems aim to provide more control over the style, timbre, and emotional content of synthesized speech, enabling applications such as voice cloning and human-like speech generation for chatbots.
Integration and Application: There is a growing emphasis on integrating TTS systems into broader applications, such as synthetic audio conversations and industry-level generative speech applications. These integrations are designed to enhance the robustness and adaptability of audio-based AI systems, making them more suitable for real-world use cases.
Noteworthy Papers
Sample-Efficient Diffusion for Text-To-Speech Synthesis: Introduces a novel diffusion architecture that achieves state-of-the-art performance with far less training data, significantly advancing the efficiency of TTS systems.
Fast, High-Quality and Parameter-Efficient Articulatory Synthesis using Differentiable DSP: Proposes a parameter-efficient DDSP vocoder that improves synthesis quality and speed, making it a promising approach for high-quality TTS.
FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications: Presents a comprehensive framework for diverse and personalized speech generation, showcasing strong in-context learning capabilities for industry applications.
