Design and Validation of a System to Synchronize Speech Recognition and Eye-Tracking Measurements
Bibliographic record
Abstract
Dementia and other forms of cognitive decline can cause aging adults to have to leave their homes and enter care facilities. These declines can affect eye movements and speech patterns. This paper proposes a system that combines and synchronizes speech recognition and eye-tracking analysis for early-stage Alzheimer's diagnosis through a computer-based analysis system. The goal is to enable the combination of these two measures within a system such as a machine learning model that can simultaneously analyze multiple types of data. This can improve the prediction performance compared to previous studies. However, before building and training the model, it is crucial to synchronize the speech and eye-tracking data streams accurately. This paper focuses on the design and validation of the proposed synchronization task to align eye tracking results from a gaze tracking system with spoken words identified by a speech recognition system. The synchronization task involves measuring the relative and absolute delay between two vision tracking systems: Tobii Eye-tracker, Webcam Eye-tracker, and recognized speech stream. A pilot sample of 9 subjects was used to validate the synchronization task. The study recorded 90 samples, and the results show that the proposed task is feasible for accurately synchronizing the data streams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".