arXiv:2606.07080v1 Announce Type: cross Abstract: We present dots.tts, a 2B-parameter continuous autoregressive text-to-speech (TTS) foundation model that models speech in a continuous latent space. Compared with existing continuous autoregressive models, our key innovations are threefold. First, we train an AudioVAE with multiple objectives to build a semantically structured and prediction-friendly continuous speech space. Second, we use full-history conditioning in the flow-matching head to preserve long-range consistency and reduce drift during generation. Third, we apply reward-free self-c
The continuous autoregressive text-to-speech (TTS) foundation model represents a significant advancement in speech synthesis technology, building on recent breakthroughs in large language models and generative AI.
This innovation pushes the frontier of human-computer interaction, enabling more natural and expressive AI-generated speech with potential applications across various industries and government functions.
The development of a 2B-parameter continuous autoregressive TTS model with improved semantic structuring, long-range consistency, and reward-free self-correction fundamentally changes the capabilities and quality expectations for speech synthesis.
- · AI companies working on multimodal models
- · Content creation platforms
- · Virtual assistant developers
- · Accessibility technology providers
- · Companies with less advanced TTS offerings
- · Traditional voice acting for some use cases
- · Small-scale speech synthesis research lacking resources for large models
More realistic and versatile AI voices will become ubiquitous in digital interfaces and automated services.
This improved speech synthesis will enable new forms of human-computer interaction and content delivery, potentially increasing disinformation vectors.
The enhanced realism could blur the lines between human and AI communication further, necessitating new authenticity verification methods for audio content.
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Read at arXiv cs.AI