Multimodal Physiological Quality-of-Experience Assessment of Text-to-Speech Systems
Bibliographic record
Abstract
With the growing complexity of various text-to-speech systems, it is becoming more important to understand the underlying perceptual and judgement processes that drive user Quality-of-Experience (QoE) perception. Typical QoE assessment techniques, such as listening tests with self-report ratings, are useful but provide limited insight into these underlying processes. Recent advances in neuroimaging and physiological monitoring technologies, however, have opened new doors and allowed us to better understand and measure QoE perception. In this paper, we explore the use of two neuroimaging techniques, namely electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), to better understand neuronal and cerebral haemodynamic changes resultant from synthesized speech of varying quality. Neural correlates of several QoE dimensions were derived and validated on the publicly available PhySyQX database. Fusion of EEG, fNIRS, and fNIRS-derived physiological parameters, combined with conventional features extracted from the synthesized speech signal showed to accurately represent several QoE dimensions, including those related to listener affective states. It is hoped that these findings will help researchers build better instrumental QoE models that incorporate technological, contextual, and human influence factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".