# MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network

arXiv: https://arxiv.org/abs/2103.00110 · HTML: https://mlsanity.com/paper/2103.00110/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2021.

## Built on by (most cited first, 30 of 37)

- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs](https://mlsanity.com/paper/2201.11972/) (2022)
- [Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features](https://mlsanity.com/paper/2111.02363/) (2021)
- [LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech](https://mlsanity.com/paper/2110.09103/) (2021)
- [Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech](https://mlsanity.com/paper/2111.04040/) (2021)
- [Utilizing Self-supervised Representations for MOS Prediction](https://mlsanity.com/paper/2104.03017/) (2021)
- [SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis](https://mlsanity.com/paper/2204.03040/) (2022)
- [SQuId: Measuring Speech Naturalness in Many Languages](https://mlsanity.com/paper/2210.06324/) (2022)
- [Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling](https://mlsanity.com/paper/2205.12986/) (2022)
- [InQSS: a speech intelligibility and quality assessment model using a multi-task learning network](https://mlsanity.com/paper/2111.02585/) (2021)
- [DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores](https://mlsanity.com/paper/2204.03219/) (2022)
- [MOSPC: MOS Prediction Based on Pairwise Comparison](https://mlsanity.com/paper/2306.10493/) (2023)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [MTI-Net: A Multi-Target Speech Intelligibility Prediction Model](https://mlsanity.com/paper/2204.03310/) (2022)
- [ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis](https://mlsanity.com/paper/2203.10473/) (2022)
- [SVSNet: An End-to-end Speaker Voice Similarity Assessment Model](https://mlsanity.com/paper/2107.09392/) (2021)
- [HASA-net: A non-intrusive hearing-aid speech assessment network](https://mlsanity.com/paper/2111.05691/) (2021)
- [Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata](https://mlsanity.com/paper/2309.09548/) (2023)
- [Resource-Efficient Fine-Tuning Strategies for Automatic MOS Prediction in Text-to-Speech for Low-Resource Languages](https://mlsanity.com/paper/2305.19396/) (2023)
- [Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities](https://mlsanity.com/paper/2206.13272/) (2022)
- [Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features](https://mlsanity.com/paper/2211.00342/) (2022)
- [APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech](https://mlsanity.com/paper/2504.20447/) (2025)
- [ORCA: Open-ended Response Correctness Assessment for Audio Question Answering](https://mlsanity.com/paper/2512.09066/) (2025)
- [Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model](https://mlsanity.com/paper/2308.09262/) (2023)
- [EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events](https://mlsanity.com/paper/2309.01480/) (2023)
- [It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation](https://mlsanity.com/paper/2310.00486/) (2023)
- [JSQA: Speech Quality Assessment with Perceptually-Inspired Contrastive Pretraining Based on JND Audio Pairs](https://mlsanity.com/paper/2507.11636/) (2025)
- [Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings](https://mlsanity.com/paper/2509.03292/) (2025)
- [Improving Self-Supervised Learning-based MOS Prediction Networks](https://mlsanity.com/paper/2204.11030/) (2022)
- [Comparison of Speech Representations for the MOS Prediction System](https://mlsanity.com/paper/2206.13817/) (2022)

## Abstract (arXiv)

Mean opinion score (MOS) is a popular subjective metric to assess the quality of synthesized speech, and usually involves multiple human judges to evaluate each speech utterance. To reduce the labor cost in MOS test, multiple methods have been proposed to automatically predict MOS scores. To our knowledge, for a speech utterance, all previous works only used the average of multiple scores from different judges as the training target and discarded the score of each individual judge, which did not well exploit the precious MOS training data. In this paper, we propose MBNet, a MOS predictor with a mean subnet and a bias subnet to better utilize every judge score in MOS datasets, where the mean subnet is used to predict the mean score of each utterance similar to that in previous works, and the bias subnet to predict the bias score (the difference between the mean score and each individual judge score) and capture the personal preference of individual judges. Experiments show that compared with MOSNet baseline that only leverages mean score for training, MBNet improves the system-level spearmans rank correlation co-efficient (SRCC) by 2.9% on VCC 2018 dataset and 6.7% on VCC 2016 dataset.