3.4. Multi-lingual Speech Recognition

In order to compare to prior work on multilingual speech
recognition, we report results on two low-data benchmarks:
Multilingual LibriSpeech (MLS) (Pratap et al., 2020b) and
VoxPopuli (Wang et al., 2021) in Table 3.
Whisper performs well on Multilingual LibriSpeech, out-
performing XLS-R (Babu et al., 2021), mSLAM (Bapna

Figure 2. Zero-shot Whisper models close the gap to human
robustness. Despite matching or outperforming a human on Lib-
riSpeech dev-clean, supervised LibriSpeech models make roughly
twice as many errors as a human on other datasets demonstrating
their brittleness and lack of robustness. The estimated robustness
frontier of zero-shot Whisper models, however, includes the 95%
conﬁdence interval for this particular human.
RobustSpeechRecognitionviaLarge-ScaleWeakSupervision