From Awareness to Adherence: Bridging the Context Gap in Spoken Dialogue Systems via Context-Aware Decoding
arXiv:2606.16472v1 Announce Type: new Abstract: Despite the success of end-to-end (E2E) spoken dialogue systems, maintaining strict context adherence in multi-round conversations remains a challenge. While prior works attribute these failures to models forgetting dialogue history, we highlight an equally critical but overlooked bottleneck: a gap between latent context awareness and active adherence. Although models internally recognize relevant past utterances, strong parametric priors often overshadow these signals during decoding. To bridge this gap, we propose an audio-adapted Context-Aware
The rapid advancement and widespread deployment of end-to-end spoken dialogue systems are exposing critical limitations in context management, driving urgent research into improved decoding mechanisms to enhance user experience and reliability.
Improving context adherence in spoken dialogue systems is crucial for their commercial viability and expands their applicability in complex, multi-turn interactions, making them more useful for a wider range of enterprise and consumer applications.
The ability of AI models to maintain consistent context across conversations improves, leading to more natural and effective interactions and reducing frustration for users.
- · AI developers
- · Customer service platforms
- · Voice assistant providers
- · Users of spoken AI systems
- · Companies with poor conversational AI
- · Manual customer support (long-term)
Spoken dialogue systems become significantly more reliable and capable in complex, multi-turn conversations.
Increased user trust and adoption of voice interfaces across various industries, from e-commerce to healthcare.
Enhanced human-computer interaction paradigms, accelerating the integration of AI into daily tasks and professional workflows through intuitive voice commands.
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Read at arXiv cs.CL