Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation
arXiv:2605.29430v1 Announce Type: new Abstract: Automatic speech recognition (ASR) is a core component of human--computer interaction and an increasingly important front-end for LLM-based assistants and agents. However, most current ASR systems still follow a single-pass paradigm, which is poorly aligned with human communication, where misunderstandings are resolved through iterative clarification and refinement. This mismatch makes it difficult to correct meaning-critical errors once they occur. Meanwhile, token-level metrics such as WER or CER cannot adequately reflect such a problem. To add
The increasing integration of ASR with LLM-based assistants and agents highlights the immediate need for more robust, human-like interactive speech recognition to overcome current system limitations.
Improving ASR to handle iterative clarification and semantic understanding will significantly enhance human-computer interaction, impacting the effectiveness and adoption of AI assistants and agents across various sectors.
ASR systems will move beyond simple single-pass transcription to more sophisticated, iterative, and context-aware communication, drastically improving user experience and allowing for real-time error correction based on meaning.
- · AI assistant developers
- · Customer service industries
- · Voice interface providers
- · LLM developers
- · Legacy ASR providers
- · Companies reliant on non-interactive voice systems
More natural and efficient voice interactions with AI systems will become commonplace.
This improved interaction could accelerate the adoption and sophistication of AI agents in critical professional and personal domains.
Enhanced interactive ASR might blur the lines between human and AI communication further, raising new ethical and societal questions about AI integration.
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Read at arXiv cs.AI