# The VoiceMOS Challenge 2022

arXiv: https://arxiv.org/abs/2203.11389 · HTML: https://mlsanity.com/paper/2203.11389/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2022.

## Built on by (most cited first, 30 of 57)

- [The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech](https://mlsanity.com/paper/2409.09305/) (2024)
- [A Comprehensive Review of Data-Driven Co-Speech Gesture Generation](https://mlsanity.com/paper/2301.05339/) (2023)
- [SpeechLMScore: Evaluating speech generation using speech language model](https://mlsanity.com/paper/2212.04559/) (2022)
- [SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis](https://mlsanity.com/paper/2204.03040/) (2022)
- [Differentiable Reward Optimization for LLM based TTS system](https://mlsanity.com/paper/2507.05911/) (2025)
- [TTSDS -- Text-to-Speech Distribution Score](https://mlsanity.com/paper/2407.12707/) (2024)
- [Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation](https://mlsanity.com/paper/2409.16644/) (2024)
- [Fusion of Self-supervised Learned Models for MOS Prediction](https://mlsanity.com/paper/2204.04855/) (2022)
- [SQuId: Measuring Speech Naturalness in Many Languages](https://mlsanity.com/paper/2210.06324/) (2022)
- [DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores](https://mlsanity.com/paper/2204.03219/) (2022)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models](https://mlsanity.com/paper/2405.14632/) (2024)
- [FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation](https://mlsanity.com/paper/2409.02245/) (2024)
- [USAT: A Universal Speaker-Adaptive Text-to-Speech Approach](https://mlsanity.com/paper/2404.18094/) (2024)
- [From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modeling](https://mlsanity.com/paper/2510.00743/) (2025)
- [Disentanglement in a GAN for Unconditional Speech Synthesis](https://mlsanity.com/paper/2307.01673/) (2023)
- [On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis](https://mlsanity.com/paper/2307.05132/) (2023)
- [Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion](https://mlsanity.com/paper/2309.02133/) (2023)
- [On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition](https://mlsanity.com/paper/2310.08132/) (2023)
- [Fine-Tuning Text-to-Speech Diffusion Models Using Reinforcement Learning with Human Feedback](https://mlsanity.com/paper/2508.03123/) (2025)
- [GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Models](https://mlsanity.com/paper/2210.05271/) (2022)
- [Resource-Efficient Fine-Tuning Strategies for Automatic MOS Prediction in Text-to-Speech for Low-Resource Languages](https://mlsanity.com/paper/2305.19396/) (2023)
- [Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features](https://mlsanity.com/paper/2211.00342/) (2022)
- [Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection](https://mlsanity.com/paper/2306.12040/) (2023)
- [A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances](https://mlsanity.com/paper/2403.14808/) (2024)
- [LatentVoiceGrad: Nonparallel Voice Conversion with Latent Diffusion/Flow-Matching Models](https://mlsanity.com/paper/2509.08379/) (2025)
- [Objective Evaluation of Prosody and Intelligibility in Speech Synthesis via Conditional Prediction of Discrete Tokens](https://mlsanity.com/paper/2509.20485/) (2025)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech](https://mlsanity.com/paper/2603.15988/) (2026)
- [Auditing Protocol-Level Shortcuts in Large Audio Language Model Judges for Speech Evaluation](https://mlsanity.com/paper/2607.13477/) (2026)

## Abstract (arXiv)

We present the first edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthetic speech. This challenge drew 22 participating teams from academia and industry who tried a variety of approaches to tackle the problem of predicting human ratings of synthesized speech. The listening test data for the main track of the challenge consisted of samples from 187 different text-to-speech and voice conversion systems spanning over a decade of research, and the out-of-domain track consisted of data from more recent systems rated in a separate listening test. Results of the challenge show the effectiveness of fine-tuning self-supervised speech models for the MOS prediction task, as well as the difficulty of predicting MOS ratings for unseen speakers and listeners, and for unseen systems in the out-of-domain setting.