arXiv cs.AIFriday · May 29, 2026FREE

Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation

A new research paper from arXiv cs.AI, titled "Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation," introduces Agentic ASR, a closed-loop framework designed to transform automatic speech recognition (ASR) into a multi-turn refinement task. Published on May 29, 2026, this system aims to overcome the limitations of traditional single-pass ASR, which often struggles with meaning-critical errors due to its misalignment with iterative human communication. Agentic ASR combines a single-pass ASR front-end with semantic correction, intent routing, and reasoning-based editing, enabling systems to resolve misunderstandings through iterative clarification. To evaluate this new paradigm, the researchers also developed the Sentence-level Semantic Error Rate ($S^2ER$), an LLM-based metric specifically designed to reflect semantic understanding rather than just token-level accuracy like WER or CER. Complementing this, an Interactive Simulation System was introduced to provide scalable and reproducible benchmarking for interactive ASR systems. Initial experiments on multilingual, named-entity-intensive, and code-switching benchmarks demonstrated the effectiveness of this iterative interaction approach in improving speech recognition performance. This work provides a foundation for more robust and context-aware speech interfaces for LLM-based assistants and agents.