Revamping Spoken Dialogue Systems with Cause-Aware Error Recovery

Traditional cascaded Automatic Speech Recognition (ASR) and Large Language Model (LLM) pipelines have long been the backbone of industrial Spoken Dialogue Systems (SDS). Their decoupled design offers perceptual verifiability, but they come with a downside. Error propagation plagues these systems, with transcription mistakes rippling through subsequent components, ultimately degrading user interaction quality.

The Problem with Cascaded Systems

ASR-LLM systems often rely on confidence scores to filter unreliable inputs. Yet, this method falls short. It can’t detect deletion errors, nor can it differentiate between acoustic and linguistic mismatches. Both types of errors need tailored recovery strategies. How can SDSs improve without a clear understanding of the specific errors they face?

Introducing Cause-Aware Error Recovery

The paper's key contribution: a cause-aware error recovery paradigm that rethinks robustness in SDS. Unlike basic confidence filtering, this new approach introduces precision-focused detectors. These detectors dive deep into ASR's latent representations, disentangling token-level errors into perception, comprehension, and deletion failures. Why is this significant? It empowers the LLM to direct multi-turn clarification strategies, turning ambiguous signals into clear user interactions.

Improvements and Impacts

Experimental results speak volumes. This cause-aware approach more than doubles recall on domain-shift errors, achieving 57.96% compared to the baseline's 23.66%. The ablation study reveals a 30% reduction in Word Error Rate (WER) and a 17% improvement on downstream tasks over various accents, distortions, and domains. It's not just about numbers. These improvements hint at a future where SDS can handle diverse linguistic challenges with heightened precision.

The Bigger Picture

What does this mean for the future of spoken dialogue systems? By embracing diagnostic intelligence, we might finally bridge the gap between human-like understanding and machine processing. The industry should take note. As accent and domain diversity continue to grow, SDS requiring such versatility will become the norm rather than the exception.