Proactive for Uncertainty: Cause-Aware Error Diagnosis and Interactive Clarification for Spoken Dialogue Systems
arXiv:2605.25404v1 Announce Type: new Abstract: Cascaded Automatic Speech Recognition -- Large Language Model (ASR-LLM) pipelines remain popular for industrial Spoken Dialogue Systems (SDS), primarily because their decoupled design ensures perceptual verifiability. However, cascaded systems suffer from error propagation, as transcription failures inevitably cascade to subsequent components, thereby degrading the final interaction quality. Although ASR confidence scores offer a simple filter for unreliable inputs, this approach is fundamentally limited because it typically fails to detect delet
The paper outlines a method using LLMs to proactively diagnose and clarify errors in spoken dialogue systems, addressing a critical challenge in their deployment at industrial scale.
Improving the reliability and conversational quality of spoken dialogue systems is crucial for widespread adoption and for advancing the utility of AI agents in real-world applications.
This approach reduces error propagation in cascaded ASR-LLM systems, enabling more robust and user-friendly interactions by actively identifying and mitigating transcription failures.
- · AI developers
- · Customer service industries
- · Spoken Dialogue System providers
- · Voice interface users
- · Traditional ASR error handling methods
- · Systems with high error propagation
Spoken dialogue systems will become significantly more reliable and intelligent in handling user input.
This improved reliability will accelerate the deployment of complex AI agents in critical applications where accuracy is paramount.
Enhanced conversational AI will lead to deeper integration of AI into daily human-computer interactions, potentially altering user expectations for all digital interfaces.
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Read at arXiv cs.CL