  Automatic speech recognition (ASR) is improving ever more at mimicking human
speech processing. The functioning of ASR, however, remains to a large extent
obfuscated by the complex structure of the deep neural networks (DNNs) they are
based on. In this paper, we show how so-called attribution methods, that we
import from image recognition and suitably adapt to handle audio data, can help
to clarify the working of ASR. Taking DeepSpeech, an end-to-end model for ASR,
as a case study, we show how these techniques help to visualize which features
of the input are the most influential in determining the output. We focus on
three visualization techniques: Layer-wise Relevance Propagation (LRP),
Saliency Maps, and Shapley Additive Explanations (SHAP). We compare these
methods and discuss potential further applications, such as in the detection of
adversarial examples.
