Using affective brain-computer interfaces to characterize human influential factors for speech quality-of-experience perception modelling
Why this work is in the frame
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Bibliographic record
Abstract
Abstract As new speech technologies emerge, telecommunication service providers have to provide superior user experience in order to remain competitive. To this end, quality-of-experience (QoE) perception modelling and measurement has become a key priority. QoE models rely on three influence factors: technological, contextual and human. Existing solutions have typically relied on the former two and human influence factors (HIFs) have been mostly neglected due to difficulty in measuring them. In this paper, we show that measuring human affective states is important for QoE measurement and propose the use of affective brain-computer interfaces (aBCIs) for objective measurement of perceived QoE for two emerging speech technologies, namely far-field hands-free communications and text-to-speech systems. When incorporating subjectively-derived HIFs into the QoE model, gains of up to 26.3 % could be found relative to utilizing only technological factors. When utilizing HIFs derived from an electroencephalography (EEG) based aBCI, in turn, gains of up to 14.5 % were observed. These findings show the importance of using aBCIs in QoE measurement and also highlight that further improvement may be warranted once improved affective state correlates are found from EEGs and/or other neurophysiological modalities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it