We present the SUPERB challenge at SLT 2022, which aims at learning
self-supervised speech representation for better performance, generalization,
and efficiency. The challenge builds upon the SUPERB benchmark and implements
metrics to measure the computation requirements of self-supervised learning
(SSL) representation and to evaluate its generalizability and performance
across the diverse SUPERB tasks. The SUPERB benchmark provides comprehensive
coverage of popular speech processing tasks, from speech and speaker
recognition to audio generation and semantic understanding. As SSL has gained
interest in the speech community and showed promising outcomes, we envision the
challenge to uplevel the impact of SSL techniques by motivating more practical
designs of techniques beyond task performance. We summarize the results of 14
submitted models in this paper. We also discuss the main findings from those
submissions and the future directions of SSL research.

介绍了在 2022 SLT 会议上举行的超级挑战赛（SUPERB Challenge），旨在学习自监督语音表征以获得更好的性能，泛化性和效率。该挑战建立在超级基准之上，并实施度量标准来测量自监督学习表征的计算需求，并评估其在不同的超级任务上的泛化性能和性能。该挑战对流行的语音处理任务进行了全面的覆盖，从语音和说话人识别到音频生成和语义理解。