# ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications

arXiv: https://arxiv.org/abs/2203.16032 · HTML: https://mlsanity.com/paper/2203.16032/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2022.

## Built on by (most cited first, 15 of 15)

- [Perceive and predict: self-supervised speech representation based loss functions for speech enhancement](https://mlsanity.com/paper/2301.04388/) (2023)
- [On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis](https://mlsanity.com/paper/2307.05132/) (2023)
- [Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata](https://mlsanity.com/paper/2309.09548/) (2023)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [WhiSQA: Non-Intrusive Speech Quality Prediction Using Whisper Encoder Features](https://mlsanity.com/paper/2508.02210/) (2025)
- [Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model](https://mlsanity.com/paper/2308.09262/) (2023)
- [EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events](https://mlsanity.com/paper/2309.01480/) (2023)
- [Preset-Voice Matching for Privacy Regulated Speech-to-Speech Translation Systems](https://mlsanity.com/paper/2407.13153/) (2024)
- [Enhanced Generative Machine Listener](https://mlsanity.com/paper/2509.21463/) (2025)
- [Speech MOS multi-task learning and rater bias correction](https://mlsanity.com/paper/2212.01911/) (2022)
- [Personalized Audio Quality Preference Prediction](https://mlsanity.com/paper/2302.08130/) (2023)
- [A multidimensional measurement of photorealistic avatar quality of experience](https://mlsanity.com/paper/2411.09066/) (2024)
- [MMMOS: Multi-domain Multi-axis Audio Quality Assessment](https://mlsanity.com/paper/2507.04094/) (2025)
- [SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment](https://mlsanity.com/paper/2602.14785/) (2026)
- [DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization](https://mlsanity.com/paper/2608.21176/) (2026)

## Abstract (arXiv)

With the advances in speech communication systems such as online conferencing applications, we can seamlessly work with people regardless of where they are. However, during online meetings, speech quality can be significantly affected by background noise, reverberation, packet loss, network jitter, etc. Because of its nature, speech quality is traditionally assessed in subjective tests in laboratories and lately also in crowdsourcing following the international standards from ITU-T Rec. P.800 series. However, those approaches are costly and cannot be applied to customer data. Therefore, an effective objective assessment approach is needed to evaluate or monitor the speech quality of the ongoing conversation. The ConferencingSpeech 2022 challenge targets the non-intrusive deep neural network models for the speech quality assessment task. We open-sourced a training corpus with more than 86K speech clips in different languages, with a wide range of synthesized and live degradations and their corresponding subjective quality scores through crowdsourcing. 18 teams submitted their models for evaluation in this challenge. The blind test sets included about 4300 clips from wide ranges of degradations. This paper describes the challenge, the datasets, and the evaluation methods and reports the final results.