  Quantifying the confidence (or conversely the uncertainty) of a prediction is
a highly desirable trait of an automatic system, as it improves the robustness
and usefulness in downstream tasks. In this paper we investigate confidence
estimation for end-to-end automatic speech recognition (ASR). Previous work has
addressed confidence measures for lattice-based ASR, while current machine
learning research mostly focuses on confidence measures for unstructured deep
learning. However, as the ASR systems are increasingly being built upon deep
end-to-end methods, there is little work that tries to develop confidence
measures in this context. We fill this gap by providing an extensive benchmark
of popular confidence methods on four well-known speech datasets. There are two
challenges we overcome in adapting existing methods: working on structured data
(sequences) and obtaining confidences at a coarser level than the predictions
(words instead of tokens). Our results suggest that a strong baseline can be
obtained by scaling the logits by a learnt temperature, followed by estimating
the confidence as the negative entropy of the predictive distribution and,
finally, sum pooling to aggregate at word level.
