TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment
Abstract
Modern Text-to-Speech (TTS) systems increasingly leverage Large Language Model (LLM) architectures to achieve scalable, high-fidelity, zero-shot generation. However, these systems typically rely on fixed-frame-rate acoustic tokenization, resulting in speech sequences that are significantly longer than, and asynchronous with their corresponding text. Beyond computational inefficiency, this sequence length disparity often triggers hallucinations in TTS and amplifies the modality gap in spoken language modeling (SLM). In this paper, we propose a novel tokenization scheme that establishes one-to-one synchronization between continuous acoustic features and text tokens, enabling unified, single-stream modeling within an LLM. We demonstrate that these synchronous tokens maintain high-fidelity audio reconstruction and can be effectively modeled in a latent space by a large language model with a flow matching head. Moreover, the ability to seamlessly toggle speech modality within the context enables text-only guidance--a technique that blends logits from text-only and text-speech modes to flexibly bridge the gap toward text-only LLM intelligence. Experimental results indicate that our approach achieves performance competitive with state-of-the-art TTS and SLM systems while virtually eliminating content hallucinations and preserving linguistic integrity, all at a significantly reduced inference cost.
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Research summary
The paper presents TADA, a generative framework that synchronizes text and acoustic features for improved speech modeling. This innovative approach addresses key challenges in traditional TTS systems, offering a significant contribution to the field with its potential for high-fidelity, efficient speech synthesis.
Methodology
The proposed methodology introduces a novel tokenization scheme that achieves one-to-one synchronization between text and acoustic features, which is a significant advancement over traditional fixed-frame-rate approaches. The use of a dual alignment mechanism and a flow matching head within a large language model framework allows for efficient and high-fidelity speech synthesis. The architecture is well-structured, leveraging a combination of variational autoencoders and transformer-based models, which enhances the model's ability to handle both modalities concurrently. The approach to mitigate the modality gap through Speech Free Guidance (SFG) is particularly innovative, allowing for flexible integration of text and speech modalities.
Results & evaluation
The experiments conducted demonstrate the effectiveness of the proposed model against state-of-the-art TTS and SLM systems. The authors provide a comprehensive evaluation using multiple datasets and metrics, including character error rate, speaker similarity, and subjective evaluations of naturalness. The results indicate that the proposed method not only matches but often exceeds the performance of existing models, particularly in terms of reducing content hallucinations and improving inference efficiency. The extensive dataset used for training and evaluation further supports the robustness of the findings.
Reproducibility
The paper includes a link to the GitHub repository containing the code and pre-trained models, which is a positive aspect for reproducibility. However, the details regarding the training process, hyperparameters, and specific configurations could be elaborated further to enhance clarity for future researchers attempting to replicate the results.
Limitations
One limitation noted is the potential for speaker drifting during long-form generation, which suggests that while the model performs well in many scenarios, there are still challenges in maintaining speaker consistency over extended outputs. Additionally, the subjective evaluations indicate that while the model performs competitively, there is room for improvement in perceptual audio quality.
Broader impact
The implications of this research are significant for the field of speech synthesis and spoken language modeling. The ability to generate high-fidelity speech with reduced hallucinations and improved efficiency can enhance applications in voice cloning, virtual assistants, and interactive AI systems. The methodology could also pave the way for further advancements in multimodal AI systems, where seamless integration of text and speech is crucial. The paper presents TADA, a generative framework that synchronizes text and acoustic features for improved speech modeling. This innovative approach addresses key challenges in traditional TTS systems, offering a significant contribution to the field with its potential for high-fidelity, efficient speech synthesis.
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