arXiv:2606.09234v1 Announce Type: cross Abstract: Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive large language model (LLM), and a diffusion based flow-matching (FM) model, with these components trained independently. In this paper, we propose a fully end-to-end (E2E) optimization framework that unifies the training of the speech tokenizer, LLM, FM model, and an additional reward model (RM). Specifically, we first jointly optimize the tokenizer using multi-task objectives derived from reconstru
The evolution of AI models is pushing towards greater integration and efficiency, with end-to-end training being a natural progression for complex generative systems like TTS.
This breakthrough signifies a move towards more robust, higher-quality, and potentially more efficient AI systems, impacting human-computer interaction and content generation.
Current cascaded TTS pipelines with independently trained components will gradually be replaced by more integrated end-to-end training frameworks, potentially leading to more natural and expressive synthetic speech.
- · AI research labs
- · Speech technology companies
- · Content creators
- · Accessibility technology
- · Developers reliant on cascaded TTS architectures
- · Companies with less sophisticated AI integration capabilities
More natural and emotionally expressive AI-generated speech becomes widely available.
The cost and computational resources required to generate high-quality speech may decrease significantly due to improved efficiency.
Enhanced realism in synthetic voices could blur the lines between human and AI-generated audio, raising new ethical considerations regarding authenticity and deepfakes.
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Read at arXiv cs.AI