Text-to-Speech (TTS)

Report on Current Developments in Text-to-Speech (TTS) Research

General Direction of the Field

The field of Text-to-Speech (TTS) synthesis is rapidly evolving, with recent developments focusing on enhancing the efficiency, quality, and robustness of speech generation models. A notable trend is the shift towards zero-shot and non-autoregressive models, which aim to reduce the dependency on large datasets and complex training procedures while maintaining or even improving the naturalness and speaker similarity of synthesized speech.

  1. Efficiency and Speed: There is a strong emphasis on developing models that can perform TTS synthesis with minimal computational resources and time. This includes the adoption of diffusion models, which, despite their computational intensity, are being optimized for faster inference through techniques like distillation and efficient architecture design.

  2. Zero-Shot and Non-Autoregressive Models: The ability to generate high-quality speech without the need for extensive fine-tuning on specific datasets is becoming increasingly important. Models like E1 TTS and StyleTTS-ZS are leading this charge, demonstrating that it is possible to achieve naturalness and speaker similarity comparable to state-of-the-art models with significantly reduced training and inference complexity.

  3. Robustness and Naturalness: Ensuring that synthesized speech is not only accurate but also natural-sounding and robust to variations in input data is a key focus. This includes addressing issues like mispronunciations and unstable formants, as seen in the development of StableForm-TTS, which integrates source-filter theory into diffusion models to improve pronunciation stability.

  4. Integration and Simplification: Efforts are being made to simplify the integration of TTS models with other deep learning frameworks and tools, making it easier for researchers and developers to build, debug, and deploy new models. ESPnet-EZ, for example, aims to reduce the complexity of using ESPnet by providing a Python-only interface.

  5. Single-Stage Models: There is a growing interest in single-stage TTS models that can achieve high-quality speech synthesis without the need for multiple stages of processing. These models aim to streamline the architecture while maintaining or improving speech quality and intelligibility.

Noteworthy Developments

  • ESPnet-EZ: Simplifies the integration and fine-tuning of speech models, significantly reducing the amount of code and dependencies required.
  • StableForm-TTS: Addresses critical pronunciation issues in diffusion-based TTS, leading to more robust and natural-sounding speech.
  • StyleTTS-ZS: Achieves high-quality zero-shot TTS with a 90% reduction in inference speed, making it a promising alternative for large-scale applications.
  • EzAudio: Enhances text-to-audio generation with a streamlined diffusion transformer architecture, offering improved quality and efficiency.

These developments highlight the ongoing innovation in the TTS field, pushing the boundaries of what is possible with speech synthesis technology.

Sources

ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration

Improving Robustness of Diffusion-Based Zero-Shot Speech Synthesis via Stable Formant Generation

E1 TTS: Simple and Fast Non-Autoregressive TTS

StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion

Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation

EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer

DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech

Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models

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