Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously
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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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jun 2023.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 5; 2026: 2.
10 papers.
- 2023StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Modelscited by 152
- 2023Matcha-TTS: A fast TTS architecture with conditional flow matchingcited by 145
- 2025Audio-Aware Large Language Models as Judges for Speaking Stylescited by 17
- 2025Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinesecited by 4
- 2025The State Of TTS: A Case Study with Human Fooling Ratescited by 4
- 2024Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesiscited by 3
- 2025NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processingcited by 3
- 2025A Linguistically Motivated Analysis of Intonational Phrasing in Text-to-Speech Systems: Revealing Gaps in Syntactic Sensitivitycited by 1
- 2026Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languagescited by 1
- 2026Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessmentcited by 0
- 2026Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages23 Apr 2026
- 2026Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment21 Apr 2026
- 2025The State Of TTS: A Case Study with Human Fooling Rates6 Aug 2025
- 2025Audio-Aware Large Language Models as Judges for Speaking Styles6 Jun 2025
- 2025A Linguistically Motivated Analysis of Intonational Phrasing in Text-to-Speech Systems: Revealing Gaps in Syntactic Sensitivity28 May 2025
- 2025Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese16 May 2025
- 2025NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing17 Feb 2025
- 2024Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis8 Oct 2024
- 2023Matcha-TTS: A fast TTS architecture with conditional flow matching6 Sep 2023
- 2023StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models13 Jun 2023