NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR
Integrating large language models (LLMs) into automatic speech recognition (ASR) has become a mainstream paradigm in recent years. Although existing LLM-based ASR models demonstrate impressive performance on public benchmarks, their training remains predominantly data-driven, leaving key practical challenges insufficiently addressed -- particularly limited downward scalability in resource-constrained deployments and hallucinations under acoustically challenging conditions. To address these issues, we present NIM4-ASR, a production-oriented LLM-based ASR framework optimized for both efficiency and robustness. Grounded in a principled delineation of functional roles between the encoder and ...
| Authors | Yuan Xie, Jiaqi Song, Guang Qiu et al. |
|---|---|
| Primary category | eess.AS |
| Published | 20 April 2026 |
| Last updated at source | 18 June 2026 |
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