arXiv:2606.10439v1 Announce Type: cross Abstract: The rapid progress of large language models (LLMs) has opened up a new frontier for automatic speech recognition (ASR), making their effective integration a critical and challenging research direction. To this end, this work proposes a projector-based LLM-ASR framework targeting the key challenges of multilingual generalization and modality alignment. Our approach incorporates a Mixture of Experts (MoE) architecture to improve cross-lingual adaptability, and a Continuous Integrate-and-Fire (CIF) mechanism for dynamic downsampling and modality a
The rapid advancements in large language models (LLMs) are pushing researchers to integrate them effectively with automatic speech recognition (ASR) to overcome existing limitations.
Improved multilingual ASR with LLMs has significant implications for global communication, accessibility, and the deployment of AI agents across diverse linguistic contexts.
This research suggests a more robust and adaptable framework for multilingual ASR, potentially reducing a significant barrier to widespread and equitable AI deployment.
- · AI developers
- · Global businesses
- · Multilingual users
- · Speech technology sector
- · Monolingual ASR solutions
- · Companies with limited linguistic AI capabilities
Enhanced multilingual LLM-ASR will lead to more effective voice interfaces and AI assistants.
This improvement could accelerate the adoption of AI agents in non-English speaking markets, increasing global AI penetration.
Broader access to sophisticated AI via improved ASR might further entrench dominant AI platforms, potentially increasing digital divides for those without access.
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Read at arXiv cs.CL