NLP-Guided Video Thin-Slicing for Behavioral Scoring
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Solution Overview
Problem
Existing methods for thin-slicing non-cognitive, behavioral performance tasks, such as public speaking or video interviews, struggle to simultaneously model content importance and delivery coherence for unstructured spontaneous speech.
Innovation Solution
The approach involves using natural language processing (NLP) and transformer models to generate textual representations of video segments, which are then fused with visual and audio features to select an optimal slice of the video using a sparse attention machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional thin-slicing methods use random slices or fixed-length excerpts, then the processing speed and efficiency are improved, but the ability to simultaneously model content importance and delivery coherence deteriorates
Solution Approach 1:
The patent replaces conventional mechanical thin-slicing approaches (random sampling, fixed-length extraction) with an NLP-guided semantic analysis system. The transformer model analyzes textual representations of video segments to identify semantically significant portions based on content importance and delivery coherence, substituting mechanical randomness with intelligent semantic selection.
Solution Approach 2:
The patent changes the selection criteria from fixed temporal parameters (random time points, fixed durations) to semantic parameters (content importance scores, delivery coherence metrics). The sparse attention model dynamically adjusts which segments to select based on their semantic significance rather than predetermined temporal intervals.
2Measurement precision
If the full video is analyzed for scoring, then the measurement precision and comprehensive evaluation are improved, but the turnaround time and computational resources increase
Solution Approach 1:
The patent extracts only the most semantically significant portions of the video for detailed analysis. The sparse attention model identifies and extracts key segments that contain the most important content and delivery information, discarding redundant portions. This extraction approach maintains evaluation accuracy while reducing processing time and computational resources.
Solution Approach 2:
The patent segments the video into multiple portions and applies different processing strategies to each segment. The transformer model processes textual representations of segments in parallel, and the sparse attention model selectively combines results from important segments, enabling efficient processing of full videos through divided analysis.
3Loss of information
If attention-based mechanisms are used to detect slices of interest, then the content relevance is improved, but the ability to model delivery coherence deteriorates
Solution Approach 1:
The patent merges content analysis and delivery analysis into a unified semantic evaluation framework. The transformer model processes both the textual content and delivery characteristics together, and the sparse attention model jointly optimizes for both content importance and delivery coherence when selecting segments to analyze.
Data Source
AI summary
Data is received that encapsulates a video of a subject performing a task. This video is used to generate a transcript using an automatic speech recognition (ASR) system. A plurality of text segments are generated from the transcript and then tokenized. A textual representation of each segment is extracted by a transformer model using the tokenized text segment (i.e., the tokens corresponding to the text segment). Thereafter, for each segment, a fused representation derived from the textual representations and corresponding visual and audio features from the video is generated. A sparse attention machine learning model then selects an optimal slice of the video based on the fused representations. The optimal slice can then be input into one or more machine learning models trained to characterize performance of the task by the subject.