arXiv:2606.13464v1 Announce Type: new Abstract: Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved in long interactions, ASR correction requires conversation-level contextual evidence. Existing ASR correction methods often rely on the current hypothesis or concatenate raw dialogue history. In such contexts, sparse correction evidence can be difficult to locate amid redundancy and noise. Addressing these challenges, we propose an ontology memory-augmented ASR correcti
The increasing complexity and length of human-AI interactions, especially in multimodal settings, necessitate more robust ASR correction methods that can leverage conversational context effectively.
Improving ASR accuracy in long, interleaved text-speech conversations is crucial for the development of highly capable AI assistants and agents, enhancing usability and reliability in critical applications.
This research shifts ASR correction from isolated utterances to conversation-level contextual understanding, allowing for more intelligent and accurate processing of human-AI dialogue.
- · AI assistant developers
- · Customer service platforms
- · Generative AI companies
- · Speech technology researchers
More natural and reliable human-AI interactions will become possible.
This improved interaction quality will accelerate enterprise adoption of AI agents for complex tasks.
The enhanced conversational intelligence could lead to new forms of AI-driven collaborative work and knowledge management.
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Read at arXiv cs.CL