Research Paper#Automatic Speech Recognition (ASR), Large Language Models (LLMs), Contextual Biasing, Hotword Retrieval, Reinforcement Learning🔬 ResearchAnalyzed: Jan 4, 2026 00:02
Contextual Biasing for LLM-Based ASR
Analysis
This paper addresses the challenge of contextual biasing, particularly for named entities and hotwords, in Large Language Model (LLM)-based Automatic Speech Recognition (ASR). It proposes a two-stage framework that integrates hotword retrieval and LLM-ASR adaptation. The significance lies in improving ASR performance, especially in scenarios with large vocabularies and the need to recognize specific keywords (hotwords). The use of reinforcement learning (GRPO) for fine-tuning is also noteworthy.
Key Takeaways
- •Proposes a two-stage framework for contextual biasing in LLM-based ASR.
- •Integrates hotword retrieval with LLM-ASR adaptation.
- •Employs robustness-aware data augmentation and fuzzy matching for hotword retrieval.
- •Uses Generative Rejection-Based Policy Optimization (GRPO) for fine-tuning.
- •Achieves significant keyword error rate reduction while maintaining sentence accuracy.
Reference
“The framework achieves substantial keyword error rate (KER) reductions while maintaining sentence accuracy on general ASR benchmarks.”