The paper details a new approach to mitigating false wake-up activations in conversational AI systems. The core of the solution is the Feedback-Driven Adaptive Self-Correcting Inference Layer (ASCIL), a post-ASR correction framework. ASCIL re-evaluates wake-up intent before response generation by fusing acoustic embeddings, linguistic cues, device context, and patterns from past misclassifications. The system interprets implicit signals, such as hesitation and silence, and explicit signals like cancellation and repetition, as indicators of potential misclassification. These signals drive online pattern updates without manual annotation. The intentional/unintentional reference labels used for offline evaluation are human-annotated.
Evaluations were conducted on a dataset of 3,667 interactions with human-annotated labels across 14 acoustic and contextual conditions. ASCIL achieved 54.27% relative error reduction on a session-disjoint subset constructed from baseline failures. At a threshold of 0.90, the system achieved up to 24.39% relative error reduction on the issue-tagged evaluation slice. The median added latency was below 60 ms in the reported benchmark.
ASCIL generalizes from prior errors, applies corrective adjustments at inference time, and continuously updates in parallel with natural-language execution. This adaptive learning capability is critical for handling the variability in human speech and the nuances of conversational interactions. The system’s ability to learn and adapt in real-time addresses a persistent challenge in deploying conversational AI in production environments.
The research focuses on improving the reliability of wake-up systems, a key factor in user experience and system efficiency. Reducing false activations minimizes unnecessary responses and improves the overall effectiveness of the assistant. Source: https://arxiv.org/abs/2609.12469
