Writing study: TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems
← ICLR 2026 oral writing corpus
Christoph Minixhofer, Ondrej Klejch, Peter Bell · Official ICLR 2026 oral record
Figure, table and section ledger
Editorial contribution type: Datasets, benchmarks & measurement resources (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 20 physical pages
- Before references / appendix
- ≈ 10.11 page-equivalents
- Main figures / tables
- 3 / 5
- Attached appendix figures / tables
- 5 / 3
- Attached appendix span
- ≈ 5.89 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1 INTRODUCTION | 1 | ≈ 2.14 pp. | 10.7% |
| 2 TTSDS2 | 3 | ≈ 1.91 pp. | 9.55% |
| 3 CORRELATIONS WITH LISTENING TESTS ACROSS DATASETS | 5 | ≈ 2.93 pp. | 14.65% |
| 4 MULTILINGUAL & RECURRING EVALUATION | 8 | ≈ 1.8 pp. | 9% |
| 5 LIMITATIONS | 10 | ≈ 0.22 pp. | 1.1% |
| 6 ETHICS STATEMENT | 10 | ≈ 0.22 pp. | 1.1% |
| 7 CONCLUSION | 10 | ≈ 0.37 pp. | 1.85% |
| REFERENCES | 11 | ≈ 4 pp. | 20% |
| A EVALUATED TTS SYSTEMS AND PARITY WITH REAL SPEECH | 15 | ≈ 1 pp. | 5% |
| B LISTENING TEST DETAILS | 16 | ≈ 2 pp. | 10% |
| C DOMAIN-WISE MOS, CMOS, SMOS AND TTSDS2 RESULTS | 18 | ≈ 1 pp. | 5% |
| D TTSDS2 FACTORS | 19 | ≈ 0.26 pp. | 1.3% |
| E CORRELATIONS BETWEEN OBJECTIVE AND SUBJECTIVE METRICS | 19 | ≈ 0.74 pp. | 3.7% |
| F COMPUTE | 20 | ≈ 0.14 pp. | 0.7% |
| G LANGUAGE DISTANCE VISUALISATION | 20 | ≈ 0.34 pp. | 1.7% |
| H FILTERING OF CONTROVERSIAL CONTENT | 20 | ≈ 0.41 pp. | 2.05% |
All detected figure and table locations
Main text
Argument and organization
- What existing speech metrics measure
Introduces a reference-requirement taxonomy before proposing a replacement.
- Factorized distribution distance
Defines four feature families, normalized scores and aggregation.
- Human validation and recurring multilingual benchmark
Separates direct validation from the broader deployment pipeline.
Problem, claim and contribution
The paper motivates cross-domain metric reliability through conflicting human-parity claims, then distinguishes matching-reference, nonmatching-reference and reference-free evaluation. This taxonomy makes the actual comparison conditions legible before new scores appear.
§§1–2, pp.1–4 · Main paper
Argument and section order
Four factor families are defined before human validation. Leave-one-domain-out aggregation experiments justify the simple mean with a visible exception: learned weights outperform it on the held-out children's domain, so robustness is not universal dominance.
§2, pp.3–5; §3.5 and Tables4–5, pp.7–8 · Main paper
Figures, captions and visual explanation
The main correlation table and scatterplots complement the appendix's domain-wise raw ratings. The former uses Spearman ranking correlation and the latter Pearson correlation; keeping these names explicit avoids treating different summary statistics as inconsistent estimates.
Table3 and Figure2, p.7; Table7, p.18 · Main paper
Tables, comparisons and evidence
Two hundred listeners validate twenty systems in four English domains. The fourteen-language extension instead uses typological-distance correlations and score sanity checks, not multilingual human quality labels; the paper explicitly acknowledges this missing validation.
§3.2, p.6; §4.2, p.10 · Main paper
Appendix and reproducibility
The appendix provides survey wording, ethics approval, system modifications, per-domain uncertainty and CPU timing. It also records English-tuned controversial-content filters, exposing a selection decision that may shape multilingual representativeness.
AppendicesA–C andF–H, pp.15–20 · Main paper
Clarity and writing conventions
The limitation section clearly states that distributional similarity cannot replace listeners or detect every transcript failure. A leftover editing instruction in the pipeline introduction illustrates why final editorial review matters even in an otherwise reproducible benchmark paper.
§4, p.8; §5, p.10 · Main paper
What to reuse—and what not to infer
Transferable lesson: Validate an evaluation metric against humans under several domains, report raw ratings beside correlations, and explicitly distinguish direct validation from multilingual plausibility checks.
Boundary: The score is dataset-level rather than per-utterance correctness, and its reference distribution matters. Temporal refresh reduces some contamination risk but cannot guarantee absence of leakage. Negative WER correlations have the expected direction and should not automatically be called failures.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-09-19
- Reading status
- Full text + selected visuals inspected
- Main text
- pp.1–10, §§1–7; references pp.11–14
- Appendix / supplement
- pp.15–20, A–H: system provenance, listening instructions, ethics approval, domain results, factors, correlations, compute and filtering
- Visually inspected PDF pages
- 7, 18
- official ICLR 2026 proceedings PDF · 20 pages