LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR

Key points

• 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.1
• 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.1
• Experiments on the ASRU2019 code-switching challenge test set demonstrate that LLM-HB achieves a 20.30% relative reduction in mixed error rate (MER) over the baseline, reaching 5.85% MER under a 15-distractor hotword setting.1
• We further release the extracted ASRU2019 hotword list to support future research.1

Sources (1)

• [1]LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 11:00 AM

  We propose LLM-HB, a language-aware large language model-guided hotword biasing framework for code-switching ASR (CS-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.

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