AIBullisharXiv – CS AI · Apr 136/10
🧠
Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition
Researchers propose Interactive ASR, a new framework that combines semantic-aware evaluation using LLM-as-a-Judge with multi-turn interactive correction to improve automatic speech recognition beyond traditional word error rate metrics. The approach simulates human-like interaction, enabling iterative refinement of recognition outputs across English, Chinese, and code-switching datasets.