INTERSPEECH 2022 Audio Deep Packet Loss Concealment Challenge
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Apr 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 2.
6 papers.
- 2022Latent-Domain Predictive Neural Speech Codingcited by 7
- 2024Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation Networkcited by 4
- 2024Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpaintingcited by 2
- 2025High-Fidelity Speech Enhancement via Discrete Audio Tokenscited by 2
- 2026UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinationscited by 0
- 2026Self-Supervised Test-Time Tuning for Packet Loss Concealmentcited by 0
- 2026Self-Supervised Test-Time Tuning for Packet Loss Concealment2 Jul 2026
- 2026UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations16 Apr 2026
- 2025High-Fidelity Speech Enhancement via Discrete Audio Tokens2 Oct 2025
- 2024Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation Network27 Jun 2024
- 2024Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting30 May 2024
- 2022Latent-Domain Predictive Neural Speech Coding18 Jul 2022