Comprehend and Talk: Text to Speech Synthesis via Dual Language Modeling
Abstract
Existing Large Language Model (LLM) based autoregressive (AR) text-to-speech (TTS) systems, while achieving state-of-the-art quality, still face critical challenges. The foundation of this LLM-based paradigm is the discretization of the continuous speech waveform into a sequence of discrete tokens by neural audio codec. However, single codebook modeling is well suited to text LLMs, but suffers from significant information loss; hierarchical acoustic tokens, typically generated via Residual Vector Quantization (RVQ), often lack explicit semantic structure, placing a heavy learning burden on the model. Furthermore, the autoregressive process is inherently susceptible to error accumulation, which can degrade generation stability. To address these limitations, we propose CaT-TTS, a novel framework for robust and semantically-grounded zero-shot synthesis. First, we introduce S3Codec, a split RVQ codec that injects explicit linguistic features into its primary codebook via semantic distillation from a state-of-the-art ASR model, providing a structured representation that simplifies the learning task. Second, we propose an ``Understand-then-Generate'' dual-Transformer architecture that decouples comprehension from rendering. An initial ``Understanding'' Transformer models the cross-modal relationship between text and the audio's semantic tokens to form a high-level utterance plan. A subsequent ``Generation'' Transformer then executes this plan, autoregressively synthesizing hierarchical acoustic tokens. Finally, to enhance generation stability, we introduce Masked Audio Parallel Inference (MAPI), a nearly parameter-free inference strategy that dynamically guides the decoding process to mitigate local errors.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this work is the introduction of CaT-TTS, a robust framework for text-to-speech synthesis that leverages a dual-transformer architecture and a semantically aware codec to achieve state-of-the-art performance in zero-shot voice cloning. This paper significantly advances the field of TTS by addressing critical challenges in representation and generation, providing a structured approach that enhances both the intelligibility and expressiveness of synthesized speech.
Methodology
The paper presents a novel framework, CaT-TTS, which integrates a split residual vector quantization codec (S3Codec) with a dual-transformer architecture to enhance text-to-speech synthesis. The methodology effectively decouples the understanding of text and the generation of speech, addressing key challenges in existing autoregressive models. The introduction of Masked Audio Parallel Inference (MAPI) is particularly noteworthy as it aims to mitigate error accumulation during the synthesis process. The use of semantic distillation from a state-of-the-art ASR model to enhance the codec's linguistic features is a significant innovation, providing a structured representation that aids in the learning task.
Results & evaluation
The experiments are comprehensive, utilizing a large dataset of approximately 200k hours of labeled speech for training. The evaluation metrics, including word error rate (WER) and speaker similarity (SIM), are appropriate for assessing the performance of TTS systems. The results demonstrate that CaT-TTS achieves state-of-the-art performance in zero-shot voice cloning, showcasing its effectiveness in both intelligibility and speaker similarity. The ablation studies further validate the contributions of the proposed methods, particularly the semantic guidance and MAPI.
Reproducibility
The paper provides detailed implementation information, including model architecture, training objectives, and evaluation setups. However, the absence of a publicly available code repository or demo URL limits the reproducibility of the results. The detailed descriptions of the training process and evaluation metrics are commendable, yet access to the actual code would enhance reproducibility.
Limitations
One limitation is the reliance on a large dataset for training, which may not be feasible for all researchers. Additionally, while the proposed methods show significant improvements, the paper does not extensively discuss potential drawbacks or scenarios where the model might underperform. The complexity of the model may also pose challenges in real-time applications.
Broader impact
The advancements in TTS synthesis presented in this paper have the potential to significantly impact various applications, including virtual assistants, audiobooks, and accessibility tools for individuals with speech impairments. The ability to synthesize speech that closely mimics human intonation and emotion can enhance user experience in interactive systems. The main contribution of this work is the introduction of CaT-TTS, a robust framework for text-to-speech synthesis that leverages a dual-transformer architecture and a semantically aware codec to achieve state-of-the-art performance in zero-shot voice cloning. This paper significantly advances the field of TTS by addressing critical challenges in representation and generation, providing a structured approach that enhances both the intelligibility and expressiveness of synthesized speech.
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