  End-to-end automatic speech recognition (ASR) commonly transcribes audio
signals into sequences of characters while its performance is evaluated by
measuring the word-error rate (WER). This suggests that predicting sequences of
words directly may be helpful instead. However, training with word-level
supervision can be more difficult due to the sparsity of examples per label
class. In this paper we analyze an end-to-end ASR model that combines a
word-and-character representation in a multi-task learning (MTL) framework. We
show that it improves on the WER and study how the word-level model can benefit
from character-level supervision by analyzing the learned inductive preference
bias of each model component empirically. We find that by adding
character-level supervision, the MTL model interpolates between recognizing
more frequent words (preferred by the word-level model) and shorter words
(preferred by the character-level model).
