FEW-SHOT AND PSEUDO-LABEL GUIDED SPEECH QUALITY EVALUATION WITH LARGE LANGUAGE MODELS
Ryandhimas E. Zezario, Szu-Wei Fu, Sabato Marco Siniscalchi, Hsin-Min Wang
Abstract
In this paper, we introduce GatherMOS, a novel framework that leverages large language models (LLM) as meta-evaluators to aggregate diverse signals into quality predictions. GatherMOS integrates lightweight acoustic descriptors with pseudo-labels from DNSMOS and VQScore, enabling the LLM to reason over heterogeneous inputs and infer perceptual mean opinion scores (MOS). We further explore both zero-shot and few-shot in-context learning setups, showing that zero-shot GatherMOS maintains stable performance across diverse conditions, while few-shot guidance yields large gains when support samples match the test conditions. Experiments on the VoiceBank-DEMAND dataset demonstrate that GatherMOS consistently outperforms DNSMOS, VQScore, naive score averaging, and even learning-based models such as CNN-BLSTM and MOS-SSL when trained under limited labeled-data conditions. These results highlight the potential of LLM-based aggregation as a practical strategy for non-intrusive speech quality evaluation.
BibTeX
@inproceedings{icassp2026_fewshotandpseudo,
title = {FEW-SHOT AND PSEUDO-LABEL GUIDED SPEECH QUALITY EVALUATION WITH LARGE LANGUAGE MODELS},
author = {Ryandhimas E. Zezario and Szu-Wei Fu and Sabato Marco Siniscalchi and Hsin-Min Wang},
booktitle = {ICASSP 2026},
year = {2026}
}