LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR
Code-switching (CS) speech, characterized by language alternation within a single utterance, is prevalent in multilingual communities and poses challenges to automatic speech recognition (ASR) due to rapid language transitions. We propose LLM-HB, a language-aware large language model-guided hotword biasing framework for code-switching ASR (CS-ASR). The framework incorporates a mixture-of-experts (MoE) adaptor and an auxiliary language prediction head to better capture and differentiate multilingual representations, while hotwords are injected through LLM-based prompting. Experiments on the ASRU2019 code-switching challenge test set demonstrate that LLM-HB achieves a 20.30% relative reduct...
| Authors | Yuxuan He, Genshun Wan, Pengcheng Li et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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