arXiv:2508.07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency scale with transcript length. A natural alternative, CTC-style non-autoregressive (NAR) systems avoid this bottleneck but their conditional independence assumption sacrifices transcript-level generative modeling. Masked diffusion language models (e.g., LLaDA, MDLM) offer a competitive NAR text-generation approach. We ask whether such models can bring NAR ASR into the accuracy regime of strong AR ASR
The proliferation of generative AI models and the increasing demand for real-time, low-latency AI applications are driving innovations in efficient ASR architectures.
This development addresses a critical bottleneck in deploying highly accurate, real-time multilingual ASR systems, which will accelerate the broader adoption of voice interfaces and AI agents.
The trade-off between ASR accuracy and inference latency is being significantly reduced, enabling the integration of high-quality speech recognition into latency-sensitive applications previously constrained by autoregressive models.
- · AI Agent developers
- · Voice interface providers
- · Speech technology companies
- · Multilingual communication platforms
- · Companies reliant solely on traditional autoregressive ASR
- · Cloud providers with inefficient ASR offerings
Increased practical deployment of sophisticated real-time voice AI across various industries.
Acceleration of AI agent development due to more reliable and faster voice interaction capabilities.
Enhanced accessibility and multilingual communication fostering new forms of digital interaction and global collaboration.
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Read at arXiv cs.LG