In the evolving landscape of speech synthesis, a significant shift towards discrete speech tokens as intermediate features has emerged. This article highlights an innovative research contribution presented at the Interspeech 2024 conference, focusing on a high-fidelity Text-to-Speech (TTS) framework that utilizes discrete tokens for enhanced performance. The adoption of discrete tokens in TTS offers several advantages over traditional continuous domain modeling, particularly in simplifying the representation of one-to-many mappings through a categorical distribution, addressing the complexities of continuous domain generative modeling.
Key Features and Benefits
-
Simplified Representation: Discrete tokens enable a more straightforward handling of one-to-many mappings by leveraging categorical distributions, simplifying the model’s ability to learn from and generate speech.
-
Integration of Specialized Schemes: The discrete output space facilitates the incorporation of various advanced techniques, notably recent advancements in large language models (LLM) such as masked language models (MLM), which can significantly enhance the TTS framework’s capabilities.
-
Robust Alignment Modeling: The discrete token approach simplifies the adoption of transducers within the TTS framework, potentially leading to more robust alignment models that improve the overall quality and naturalness of synthesized speech.
The Two-Stage Procedure
The proposed TTS framework follows a structured two-stage approach:
-
Interpreting: This stage involves the conversion of text into semantic tokens, focusing on extracting meaningful linguistic content that is crucial for enhancing the intelligibility of synthesized speech.
-
Speaking: In the subsequent stage, the semantic tokens are transformed into acoustic tokens, which are then used to generate high-fidelity speech. This stage ensures that the synthesized speech not only carries the intended semantic content but also closely mimics natural speech patterns.
The Proposed Method
The overall architecture of the proposed model is designed to optimize the use of semantic and acoustic tokens, showcasing a significant advancement in the field of TTS. The model’s effectiveness is underpinned by its ability to:
-
Leverage Discrete Tokens: By focusing on discrete tokens, the model can more efficiently capture the essential aspects of speech, leading to enhanced performance in terms of both fidelity and naturalness.
-
Incorporate Recent Advancements: The integration of recent advancements in large language models, particularly masked language models, allows for a more nuanced and contextually aware speech synthesis, potentially resulting in more lifelike and contextually appropriate speech outputs.
-
Robust Alignment: The model’s use of transducers facilitates the development of robust alignment models, which are crucial for aligning the semantic and acoustic components of speech, thereby improving the overall quality of the synthesized speech.
Conclusion
The proposed high-fidelity TTS framework, by leveraging discrete tokens, represents a significant leap forward in the field of speech synthesis. This innovative approach not only simplifies the representation of speech but also opens up new possibilities for integrating advanced language models and enhancing the naturalness and intelligibility of synthesized speech. As the field continues to evolve, this research contributes valuable insights and methodologies that could shape the future of speech synthesis technologies.
Views: 0