# PLCMOS -- a data-driven non-intrusive metric for the evaluation of packet loss concealment algorithms

arXiv: https://arxiv.org/abs/2305.15127 · HTML: https://mlsanity.com/paper/2305.15127/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 7 of 7)

- [LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement](https://mlsanity.com/paper/2503.00493/) (2025)
- [ESPnet-SDS: Unified Toolkit and Demo for Spoken Dialogue Systems](https://mlsanity.com/paper/2503.08533/) (2025)
- [High-Fidelity Speech Enhancement via Discrete Audio Tokens](https://mlsanity.com/paper/2510.02187/) (2025)
- [The Limits of Reference-Free Speech Quality Metrics as Evaluators and Rewards on Modern Text-to-Speech](https://mlsanity.com/paper/2609.13150/) (2026)
- [Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions](https://mlsanity.com/paper/2509.24457/) (2025)
- [Large Speech Model Enabled Semantic Communication](https://mlsanity.com/paper/2512.04711/) (2025)
- [Self-Supervised Test-Time Tuning for Packet Loss Concealment](https://mlsanity.com/paper/2607.01823/) (2026)

## Abstract (arXiv)

Speech quality assessment is a problem for every researcher working on models that produce or process speech. Human subjective ratings, the gold standard in speech quality assessment, are expensive and time-consuming to acquire in a quantity that is sufficient to get reliable data, while automated objective metrics show a low correlation with gold standard ratings. This paper presents PLCMOS, a non-intrusive data-driven tool for generating a robust, accurate estimate of the mean opinion score a human rater would assign an audio file that has been processed by being transmitted over a degraded packet-switched network with missing packets being healed by a packet loss concealment algorithm. Our new model shows a model-wise Pearson's correlation of ~0.97 and rank correlation of ~0.95 with human ratings, substantially above all other available intrusive and non-intrusive metrics. The model is released as an ONNX model for other researchers to use when building PLC systems.