PhySyQX: A database for physiological evaluation of synthesised speech quality-of-experience
Bibliographic record
Abstract
A product's success in the market can be predicted based on the Quality-of-Experience (QoE) it offers to its users. With the burgeoning market for text-to-speech (TTS) systems, it has become extremely important to characterise new TTS systems in terms of their QoE. To this end, many objective models for quality estimation have been developed. These state-of-the art models are developed considering the system and contextual factors which influence the users' experience. Such models generally lack inputs from human factors, as these are not directly observable and are manifested inside users' brains. Therefore, in this study a multi-modal database was developed for neuro-physiological identification of the human factors which influence user perceived QoE and also to probe into the users' internal quality formation processes. It is hoped that the database will help improve the pre-existing models for quality estimation. The database utilizes neuro-physiological tools, such as electroencephalography and functional near infrared spectroscopy, to record users' brain activity while experiencing synthesised speech produced from various commercially available TTS systems. Moreover, an extensive analysis of participants' ratings has been reported in the paper. Also, the database has been made publicly available online to encourage other researchers to utilize the neuro-physiological insights while developing new quality estimation algorithms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".