  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.
