# APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech

arXiv: https://arxiv.org/abs/2504.20447 · HTML: https://mlsanity.com/paper/2504.20447/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 56 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 30 of 30)

- [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)
- [ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech](https://mlsanity.com/paper/2207.06389/) (2022)
- [MOSNet: Deep Learning based Objective Assessment for Voice Conversion](https://mlsanity.com/paper/1904.08352/) (2019)
- [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)
- [Generalization Ability of MOS Prediction Networks](https://mlsanity.com/paper/2110.02635/) (2021)
- [CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model](https://mlsanity.com/paper/2305.06908/) (2023)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network](https://mlsanity.com/paper/2103.00110/) (2021)
- [Deep Learning Based Assessment of Synthetic Speech Naturalness](https://mlsanity.com/paper/2104.11673/) (2021)
- [LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech](https://mlsanity.com/paper/2110.09103/) (2021)
- [FlashSpeech: Efficient Zero-Shot Speech Synthesis](https://mlsanity.com/paper/2404.14700/) (2024)
- [SCOREQ: Speech Quality Assessment with Contrastive Regression](https://mlsanity.com/paper/2410.06675/) (2024)
- [AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech](https://mlsanity.com/paper/1611.09207/) (2016)
- [Utilizing Self-supervised Representations for MOS Prediction](https://mlsanity.com/paper/2104.03017/) (2021)
- [Generative Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2407.21491/) (2024)
- [NORESQA: A Framework for Speech Quality Assessment using Non-Matching References](https://mlsanity.com/paper/2109.08125/) (2021)
- [Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation](https://mlsanity.com/paper/2409.16644/) (2024)
- [MOS-Bench: Benchmarking Generalization Abilities of Subjective Speech Quality Assessment Models](https://mlsanity.com/paper/2411.03715/) (2024)
- [Fusion of Self-supervised Learned Models for MOS Prediction](https://mlsanity.com/paper/2204.04855/) (2022)
- [Face-Driven Zero-Shot Voice Conversion with Memory-based Face-Voice Alignment](https://mlsanity.com/paper/2309.09470/) (2023)
- [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)
- [RAMP: Retrieval-Augmented MOS Prediction via Confidence-based Dynamic Weighting](https://mlsanity.com/paper/2308.16488/) (2023)
- [Who is Speaking Actually? Robust and Versatile Speaker Traceability for Voice Conversion](https://mlsanity.com/paper/2305.05152/) (2023)
- [PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion](https://mlsanity.com/paper/2308.11084/) (2023)
- [MOS-FAD: Improving Fake Audio Detection Via Automatic Mean Opinion Score Prediction](https://mlsanity.com/paper/2401.13249/) (2024)

## Built on by (most cited first, 2 of 2)

- [WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction](https://mlsanity.com/paper/2506.05899/) (2025)
- [AnimeScore: A Preference-Based Dataset and Framework for Evaluating Anime-Like Speech Style](https://mlsanity.com/paper/2603.11482/) (2026)

## Abstract (arXiv)

Automatic speech quality assessment aims to quantify subjective human perception of speech through computational models to reduce the need for labor-consuming manual evaluations. While models based on deep learning have achieved progress in predicting mean opinion scores (MOS) to assess synthetic speech, the neglect of fundamental auditory perception mechanisms limits consistency with human judgments. To address this issue, we propose an auditory perception guided-MOS prediction model (APG-MOS) that synergistically integrates auditory modeling with semantic analysis to enhance consistency with human judgments. Specifically, we first design a perceptual module, grounded in biological auditory mechanisms, to simulate cochlear functions, which encodes acoustic signals into biologically aligned electrochemical representations. Secondly, we propose a residual vector quantization (RVQ)-based semantic distortion modeling method to quantify the degradation of speech quality at the semantic level. Finally, we design a residual cross-attention architecture, coupled with a progressive learning strategy, to enable multimodal fusion of encoded electrochemical signals and semantic representations. Experiments demonstrate that APG-MOS achieves superior performance on two primary benchmarks. Our code and checkpoint will be available on a public repository upon publication.