Multimodal Turn-Taking Recognition for Human-Robot Interaction
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Solution Overview
Problem
Current technologies face challenges in accurately recognizing turn-taking behavior between humans and robots, primarily due to limited consideration of multimodal cues, which hinders natural interaction and effective conversation management.
Innovation Solution
An interaction apparatus that utilizes multimodal information, including images and voices, to recognize turn-taking behavior by identifying lip shapes, gestures, and voice features, allowing for accurate detection of intentions such as starting, continuing, or stopping an utterance, and determining appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional voice-based turn-taking recognition is used, then the system is simple to implement, but the accuracy of recognizing turn-taking behavior is insufficient
Solution Approach 1:
The patent combines multiple modalities (voice, lip shape, gesture) into a unified turn-taking recognition system. The recognition unit integrates information from voice features, lip shape features extracted from image data, and gesture features to comprehensively determine turn-taking behavior, thereby improving recognition accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The patent adds spatial and visual dimensions to traditional voice-based turn-taking recognition. By incorporating lip shape analysis from image data and gesture recognition, the system transitions from unimodal acoustic processing to multimodal processing that includes visual-spatial information, enabling more accurate detection of turn-taking cues.
2Measurement precision
If multiple multimodal cues are analyzed, then the recognition accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary processing of multimodal data by extracting features from voice, lip shape, and gesture simultaneously rather than sequentially. The recognition unit is configured to process multiple modalities in parallel, preparing turn-taking determination by analyzing all cues together, which reduces overall processing time while maintaining high accuracy.
Solution Approach 2:
The recognition unit autonomously determines turn-taking behavior by integrating multiple modalities without requiring external intervention or complex coordination between separate processing modules. The system self-manages the integration of voice, lip shape, and gesture features to efficiently produce turn-taking decisions.
Data Source
AI summary
Disclosed herein are an interaction apparatus and method. The interaction apparatus includes an input unit for receiving multimodal information including an image and a voice of a target to allow the interaction apparatus to interact with the target, a recognition unit for recognizing turn-taking behavior of the target using the multimodal information, and an execution unit for taking an activity for interacting with the target based on results of recognition of the turn-taking behavior.