arXiv:2606.24915v1 Announce Type: new Abstract: End-to-end automatic speech recognition systems frequently hallucinate rare entities and domain-specific terms, especially in low-resource languages. While retrieval-augmented generation frameworks can mitigate these errors using large language models, current architectures face significant challenges. They either rely on standard sparse retrieval that ignores phonetic misrecognitions or utilize heavyweight cross-modal embeddings that introduce high latency. This letter proposes a highly efficient, purely lexical error-aware framework designed to
The accelerating deployment of AI in critical applications like transcription and customer service for diverse languages highlights the immediate need for robust error correction in ASR systems.
Improving the accuracy of Automatic Speech Recognition (ASR) systems, particularly for rare entities and low-resource languages, directly impacts the reliability and utility of AI speech interfaces across various sectors.
This advancement enables AI systems to more accurately transcribe specialized terminology and less common linguistic elements, reducing hallucination and improving interaction quality.
- · AI language model developers
- · Customer service industries
- · Low-resource language communities
- · Accessibility technology providers
- · ASR systems with high error rates
- · Businesses reliant on manual transcription
ASR systems become more reliable for domain-specific and multilingual applications, expanding their utility.
Increased trust and adoption of AI-driven voice interfaces in sectors where accuracy is paramount, such as healthcare or legal.
Enhanced data generation for AI training in low-resource languages, fostering more equitable AI development and deployment globally.
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Read at arXiv cs.CL