The effects of text-to-speech system quality on emotional states and frontal alpha band power
Bibliographic record
Abstract
The tolerance limit for acceptable multimedia quality is changing as more and more high quality services approach the market. Thus, negative emotional reactions towards low quality services may cause user disappointment and are likely to increase churn rate. The current study analyzes how different levels of synthetic speech quality, obtained from different text-to-speech (TTS) systems, affect the emotional response of a user. This is achieved using two methods: subjective, by means of user reports; and neurophysiological by means of electroencephalography (EEG) analysis. More specifically, we analyzed the frontal alpha band power and correlated this with the subjective ratings based on the Self-Assessment Manikin scale. We found an increase in neuronal activity in the left frontal area with decreasing quality and argue that this is due to user disappointment with low quality TTS systems as they become harder to understand.
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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 | 0.000 | 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) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".