  End-to-end neural network systems for automatic speech recognition (ASR) are
trained from acoustic features to text transcriptions. In contrast to modular
ASR systems, which contain separately-trained components for acoustic modeling,
pronunciation lexicon, and language modeling, the end-to-end paradigm is both
conceptually simpler and has the potential benefit of training the entire
system on the end task. However, such neural network models are more opaque: it
is not clear how to interpret the role of different parts of the network and
what information it learns during training. In this paper, we analyze the
learned internal representations in an end-to-end ASR model. We evaluate the
representation quality in terms of several classification tasks, comparing
phonemes and graphemes, as well as different articulatory features. We study
two languages (English and Arabic) and three datasets, finding remarkable
consistency in how different properties are represented in different layers of
the deep neural network.
