Head Motion Estimation for Next Speaker Timing
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Solution Overview
Problem
Existing methods for estimating the next speaker and timing in multi-participant communication have low accuracy and do not effectively predict when a participant will start speaking.
Innovation Solution
An estimation apparatus and method that utilize head motion information and synchronization analysis to predict the next speaker and utterance start timing by computing six-degree-of-freedom head motion data and synchronization information between participants, employing machine learning techniques to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If head motion information and synchronization analysis are used to estimate the next speaker and timing, then prediction accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the estimation process into distinct functional units: a head motion information generation unit that processes individual participant head motions, and an estimation unit that performs synchronization analysis. This segmentation allows complex computations to be divided into manageable modules, improving prediction accuracy while organizing device complexity into structured components.
Solution Approach 2:
The head motion information generation unit performs preliminary processing of head motion data before the estimation unit conducts synchronization analysis. By pre-computing head motion information for each participant in advance, the system reduces the computational burden during the final estimation phase, thereby improving prediction accuracy without excessively increasing overall device complexity.
2Measurement precision
If six-degree-of-freedom head motion data is computed for each participant, then estimation accuracy is improved, but computational load and processing time increase
Solution Approach 1:
The system extracts only the essential head motion information needed for synchronization analysis from the six-degree-of-freedom head motion data. By selecting and processing only the relevant components of head motion data, the system maintains high estimation accuracy while reducing the overall computational load compared to processing all six degrees of freedom in full detail.
Solution Approach 2:
The system transforms raw six-degree-of-freedom head motion parameters into synchronized motion patterns that capture the essential information for next speaker prediction. By changing the parameter representation from individual head motion components to collective synchronization metrics, the system achieves accurate estimation with reduced computational requirements.
3Measurement precision
If synchronization information is computed between all participant pairs, then next speaker prediction accuracy is improved, but calculation complexity increases
Solution Approach 1:
The estimation unit performs a universal synchronization analysis that can handle any pair of participants using the same computational framework. This multi-functional approach allows the system to compute synchronization information between all participant pairs systematically, improving prediction accuracy while avoiding the need for separate specialized algorithms for each pair, thereby managing calculation complexity through methodological universality.
Data Source
AI summary
In communication performed among multiple participants, at least one of a participant who will start speaking next and a timing thereof is estimated.An estimation apparatus includes a head motion information generation unit that acquires head motion information representing head motions of communication participants in a time segment corresponding to an end time of an utterance segment and synchronization information for head motions between the communication participants, and an estimation unit that estimates at least one of the speaker of the next utterance segment following the utterance segment and the next utterance start timing following the utterance segment based on the head motion information and the synchronization information for the head motions between the communication participants.