# RAMP: Retrieval-Augmented MOS Prediction via Confidence-based Dynamic Weighting

arXiv: https://arxiv.org/abs/2308.16488 · HTML: https://mlsanity.com/paper/2308.16488/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2023.

## Built on by (most cited first, 6 of 6)

- [FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine Flow Matching](https://mlsanity.com/paper/2502.11128/) (2025)
- [SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment](https://mlsanity.com/paper/2604.25937/) (2026)
- [StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling](https://mlsanity.com/paper/2506.12570/) (2025)
- [Improving Zero-Shot Chinese-English Code-Switching ASR with kNN-CTC and Gated Monolingual Datastores](https://mlsanity.com/paper/2406.03814/) (2024)
- [APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech](https://mlsanity.com/paper/2504.20447/) (2025)
- [DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization](https://mlsanity.com/paper/2608.21176/) (2026)

## Abstract (arXiv)

Automatic Mean Opinion Score (MOS) prediction is crucial to evaluate the perceptual quality of the synthetic speech. While recent approaches using pre-trained self-supervised learning (SSL) models have shown promising results, they only partly address the data scarcity issue for the feature extractor. This leaves the data scarcity issue for the decoder unresolved and leading to suboptimal performance. To address this challenge, we propose a retrieval-augmented MOS prediction method, dubbed bf RAMP, to enhance the decoder's ability against the data scarcity issue. A fusing network is also proposed to dynamically adjust the retrieval scope for each instance and the fusion weights based on the predictive confidence. Experimental results show that our proposed method outperforms the existing methods in multiple scenarios.