  Large self-supervised pre-trained speech models have achieved remarkable
success across various speech-processing tasks. The self-supervised training of
these models leads to universal speech representations that can be used for
different downstream tasks, ranging from automatic speech recognition (ASR) to
speaker identification. Recently, Whisper, a transformer-based model was
proposed and trained on large amount of weakly supervised data for ASR; it
outperformed several state-of-the-art self-supervised models. Given the
superiority of Whisper for ASR, in this paper we explore the transferability of
the representation for four other speech tasks in SUPERB benchmark. Moreover,
we explore the robustness of Whisper representation for ``in the wild'' tasks
where speech is corrupted by environment noise and room reverberation.
Experimental results show Whisper achieves promising results across tasks and
environmental conditions, thus showing potential for cross-task real-world
deployment.
