PhySyQX: A database for physiological evaluation of synthesised speech quality-of-experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.001 |
| 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 | 0.001 | 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