RESPOND: Responsive Engagement Strategy for Predictive Orchestration and Dialogue

2026-03-23

AI Summary

This work addresses the limitations of conventional spoken dialogue systems, which often rely on rigid turn-taking mechanisms that fail to capture the natural rhythm and listener engagement characteristic of human conversation. To overcome this, the authors propose a real-time feedback generation framework grounded in streaming automatic speech recognition (ASR) and incremental semantic understanding. The system employs a tunable, two-dimensional control mechanism—modulating both response intensity and turn-taking eagerness—to dynamically determine when and how to insert conversational feedback, such as backchannel utterances or proactive turn transitions. Integrating streaming ASR, incremental semantic parsing, real-time intent prediction, and controllable policy generation, the framework demonstrates contextual awareness and adaptability to diverse social interaction scenarios, significantly enhancing dialogue fluency and user engagement across multiple conversational contexts.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Multiagent Systems: Agent Communication
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The majority of voice-based conversational agents still rely on pause-and-respond turn-taking, leaving interactions sounding stiff and robotic. We present RESPOND (Responsive Engagement Strategy for Predictive Orchestration and Dialogue), a framework that brings two staples of human conversation to agents: timely backchannels ("mm-hmm," "right") and proactive turn claims that can contribute relevant content before the speaker yields the conversational floor. Built on streaming ASR (Automatic Speech Recognition) and incremental semantics, RESPOND continuously predicts both when and how to interject, enabling fluid, listener-aware dialogue. A defining feature is its designer-facing controllability: two orthogonal dials, Backchannel Intensity (frequency of acknowledgments) and Turn Claim Aggressiveness (depth and assertiveness of early contributions), can be tuned to match the etiquette of contexts ranging from rapid ideation to reflective counseling. By coupling predictive orchestration with explicit control, RESPOND offers a practical path toward conversational agents that adapt their conversational footprint to social expectations, advancing the design of more natural and engaging voice interfaces.

Problem

• conversational agents
• turn-taking
• backchannels
• natural dialogue
• voice interfaces

Innovation

• responsive engagement
• predictive orchestration
• incremental semantics
• backchanneling
• turn-taking