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With the advances in speech communication systems such as online conferencingapplications, we can seamlessly work with people regardless of where they are.However, during online meetings, speech quality can be significantly affectedby background noise, reverberation, packet loss, network jitter, etc. Becauseof its nature, speech quality is traditionally assessed in subjective tests inlaboratories and lately also in crowdsourcing following the internationalstandards from ITU-T Rec. P.800 series. However, those approaches are costlyand cannot be applied to customer data. Therefore, an effective objectiveassessment approach is needed to evaluate or monitor the speech quality of theongoing conversation. The ConferencingSpeech 2022 challenge targets thenon-intrusive deep neural network models for the speech quality assessmenttask. We open-sourced a training corpus with more than 86K speech clips indifferent languages, with a wide range of synthesized and live degradations andtheir corresponding subjective quality scores through crowdsourcing. 18 teamssubmitted their models for evaluation in this challenge. The blind test setsincluded about 4300 clips from wide ranges of degradations. This paperdescribes the challenge, the datasets, and the evaluation methods and reportsthe final results.

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