# INTERSPEECH 2022 Audio Deep Packet Loss Concealment Challenge

arXiv: https://arxiv.org/abs/2204.05222 · HTML: https://mlsanity.com/paper/2204.05222/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2022.

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

- [Latent-Domain Predictive Neural Speech Coding](https://mlsanity.com/paper/2207.08363/) (2022)
- [Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation Network](https://mlsanity.com/paper/2406.18928/) (2024)
- [Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting](https://mlsanity.com/paper/2405.20101/) (2024)
- [High-Fidelity Speech Enhancement via Discrete Audio Tokens](https://mlsanity.com/paper/2510.02187/) (2025)
- [UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations](https://mlsanity.com/paper/2604.14606/) (2026)
- [Self-Supervised Test-Time Tuning for Packet Loss Concealment](https://mlsanity.com/paper/2607.01823/) (2026)

## Abstract (arXiv)

Audio Packet Loss Concealment (PLC) is the hiding of gaps in audio streams caused by data transmission failures in packet switched networks. This is a common problem, and of increasing importance as end-to-end VoIP telephony and teleconference systems become the default and ever more widely used form of communication in business as well as in personal usage. This paper presents the INTERSPEECH 2022 Audio Deep Packet Loss Concealment challenge. We first give an overview of the PLC problem, and introduce some classical approaches to PLC as well as recent work. We then present the open source dataset released as part of this challenge as well as the evaluation methods and metrics used to determine the winner. We also briefly introduce PLCMOS, a novel data-driven metric that can be used to quickly evaluate the performance PLC systems. Finally, we present the results of the INTERSPEECH 2022 Audio Deep PLC Challenge, and provide a summary of important takeaways.