arXiv:2604.18105v2 Announce Type: replace-cross Abstract: 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-orien
The proliferation of LLMs and the increasing demand for real-time, efficient AI applications drive the continuous research into optimizing their practical deployment.
This development addresses critical limitations of current LLM-based ASR, specifically scalability for resource-constrained environments and robustness in challenging acoustic conditions, which are key for broad adoption.
The focus on 'production-oriented' and 'customizable' solutions indicates a shift towards more practical and deployable ASR systems for a wider range of industrial and consumer applications.
- · Edge AI chip manufacturers
- · Developers of resource-constrained AI applications
- · Industries requiring robust real-time ASR
- · Companies reliant on highly centralized ASR architectures
- · Generic, unoptimized LLM-based ASR solutions
Improved performance and broader accessibility of real-time ASR in diverse environments.
Increased adoption of voice interfaces and AI assistants in edge devices and specialized industrial settings.
Enhanced human-machine interaction in critical or challenging acoustic scenarios, leading to new workflow efficiencies and safety improvements.
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