A potential concern with jointly training a single model
on many tasks and languages is the possibility of negative
transfer where interference between the learning of several
tasks results in performance worse than would be achieved
by training on only a single task or language. To investigate
whether this is occurring, we compared the performance
of models trained on just English speech recognition with
our standard multitask and multilingual training setup and
measured their average performance across our suite of zero-
shot English speech recognition benchmarks. We adjust for
the amount of FLOPs spent training on the task of English
speech recognition as only 65% of compute is spent on this
task in a joint training setup; analysis would otherwise be
confounded by under-training on the task when compared
to a same-sized English-only model.
Our results visualized in Figure 9 show that for small models
trained with moderate amounts of compute, there is indeed
RobustSpeechRecognitionviaLarge-ScaleWeakSupervision