Towards a Vowel Formant Based Quality Metric for Text-to-Speech Systems: Measuring Monophthong Naturalness
Bibliographic record
Abstract
This contribution proposes an objective, vowel formant based quality metric for assessing the naturalness of monophthongs synthesized by Text-to-Speech (TTS) systems. This could eliminate the need for time and resource-intensive perception-based mean opinion score metrics. We show that vowel space plots can serve as a basis for developing a more comprehensive, linguistically sound quality metric for TTS systems. In addition, our metric provides detailed insights on the quality of individual monophthongs, which could help with identifying areas for further optimization in TTS systems. We use a state-of-the-art neural TTS pipeline based on Tacotron and WaveGlow, trained on the LJ Speech dataset, to generate the same audio as in our validation set and compare the two sets of audio files. Our quality metric is automatically calculated using the Montreal Forced Aligner for aligning text and audio, Praat for measuring formant values and a custom R script to calculate the vowel spaces and their overlap. Our results show that the vowel spaces of the original audio and the synthesized audio overlap to a large extent, especially for the means and the central 75% of the data, with more variation when considering all data points. Furthermore, we show that there is considerable variation in the overlap of the vowel spaces of different monophthongs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".