arXiv:2509.09631v4 Announce Type: replace-cross Abstract: Zero-shot text-to-speech (TTS) has made significant progress in replicating unseen voices, yet balancing generation quality and inference efficiency remains challenging. Autoregressive models suffer from high latency, while diffusion-based approaches are constrained by training-time configurations. Moreover, most flow-based methods operate in continuous space, which introduces optimization challenges because continuous token spaces are inherently more complex than discrete ones. To address these limitations, we propose DiFlow-TTS, a nov
The continuous push for more efficient and lower-latency AI models is a constant in the rapidly evolving field of machine learning, especially for real-time applications like text-to-speech.
This development in zero-shot text-to-speech with discrete flow matching indicates progress towards more efficient and practical voice replication, crucial for pervasive AI applications.
The ability to generate high-quality, low-latency, and zero-shot voice synthesis from text becomes more efficient and less resource-intensive, broadening its application potential.
- · AI developers
- · Speech technology companies
- · Customer service platforms
- · Accessibility technology providers
- · High-latency TTS providers
- · Resource-intensive voice synthesis models
Improved user experience in applications requiring real-time, personalized voice output.
Accelerated adoption of personalized AI assistants and interfaces across various industries.
Potential for new human-computer interaction paradigms based on highly realistic and responsive synthesized voices.
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Read at arXiv cs.CL