# The VoiceMOS Challenge 2026: Evaluating Speech Enhancement, Emotional TTS and Accented TTS Systems

arXiv: https://arxiv.org/abs/2609.13792 · HTML: https://mlsanity.com/paper/2609.13792/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Vocoders and speech synthesis, one of 11 directions in Speech and audio language models.
- Builds on 25 of its 26 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Placed in

- Speech and audio language models / [Vocoders and speech synthesis](https://mlsanity.com/speech/vocoders-and-speech-synthesis/)

## Builds on (most cited first, 25 of 25)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech](https://mlsanity.com/paper/2207.01063/) (2022)
- [The VoiceMOS Challenge 2022](https://mlsanity.com/paper/2203.11389/) (2022)
- [URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement](https://mlsanity.com/paper/2406.04660/) (2024)
- [How do Voices from Past Speech Synthesis Challenges Compare Today?](https://mlsanity.com/paper/2105.02373/) (2021)
- [LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech](https://mlsanity.com/paper/2110.09103/) (2021)
- [Interspeech 2025 URGENT Speech Enhancement Challenge](https://mlsanity.com/paper/2505.23212/) (2025)
- [The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction](https://mlsanity.com/paper/2409.07001/) (2024)
- [The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains](https://mlsanity.com/paper/2310.02640/) (2023)
- [InQSS: a speech intelligibility and quality assessment model using a multi-task learning network](https://mlsanity.com/paper/2111.02585/) (2021)
- [VoxSim: A perceptual voice similarity dataset](https://mlsanity.com/paper/2407.18505/) (2024)
- [The AudioMOS Challenge 2025](https://mlsanity.com/paper/2509.01336/) (2025)
- [SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment](https://mlsanity.com/paper/2510.01812/) (2025)
- [ICASSP 2026 URGENT Speech Enhancement Challenge](https://mlsanity.com/paper/2601.13531/) (2026)
- [UrgentMOS: Unified Multi-Metric and Preference Learning for Robust Speech Quality Assessment](https://mlsanity.com/paper/2601.18438/) (2026)
- [CodecMOS-Accent: A MOS Benchmark of Resynthesized and TTS Speech from Neural Codecs Across English Accents](https://mlsanity.com/paper/2603.14328/) (2026)

## Abstract (arXiv)

We present the results of the VoiceMOS Challenge 2026, the fifth edition of a scientific challenge on automatic prediction of subjective speech assessments. After expanding the scope to music and general audio in 2025, we refocused the evaluation target on speech and organized three tracks: prediction of absolute and comparative category ratings for enhanced speech, prediction of naturalness and emotion-related tasks for emotional text-to-speech systems, and prediction of speaker and accent similarity for codec-based speech synthesis systems. The challenge attracted a total of 18 teams worldwide, with most teams successfully surpassing the provided baselines. We summarize the challenge results, representative top-performing systems, participant feedback, and directions for future editions.