# Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously

arXiv: https://arxiv.org/abs/2306.02044 · HTML: https://mlsanity.com/paper/2306.02044/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2023.

## Built on by (most cited first, 10 of 10)

- [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://mlsanity.com/paper/2306.07691/) (2023)
- [Matcha-TTS: A fast TTS architecture with conditional flow matching](https://mlsanity.com/paper/2309.03199/) (2023)
- [Audio-Aware Large Language Models as Judges for Speaking Styles](https://mlsanity.com/paper/2506.05984/) (2025)
- [Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese](https://mlsanity.com/paper/2505.11200/) (2025)
- [The State Of TTS: A Case Study with Human Fooling Rates](https://mlsanity.com/paper/2508.04179/) (2025)
- [Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis](https://mlsanity.com/paper/2410.06327/) (2024)
- [NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing](https://mlsanity.com/paper/2502.12002/) (2025)
- [A Linguistically Motivated Analysis of Intonational Phrasing in Text-to-Speech Systems: Revealing Gaps in Syntactic Sensitivity](https://mlsanity.com/paper/2505.22236/) (2025)
- [Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages](https://mlsanity.com/paper/2604.21481/) (2026)
- [Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment](https://mlsanity.com/paper/2605.00861/) (2026)

## Abstract (arXiv)

This paper emphasizes the importance of reporting experiment details in subjective evaluations and demonstrates how such details can significantly impact evaluation results in the field of speech synthesis. Through an analysis of 80 papers presented at INTERSPEECH 2022, we find a lack of thorough reporting on critical details such as evaluator recruitment and filtering, instructions and payments, and the geographic and linguistic backgrounds of evaluators. To illustrate the effect of these details on evaluation outcomes, we conducted mean opinion score (MOS) tests on three well-known TTS systems under different evaluation settings and we obtain at least three distinct rankings of TTS models. We urge the community to report experiment details in subjective evaluations to improve the reliability and interpretability of experimental results.