abstract
end-to-end training of automated speech
recognition (asr) systems requires massive
data and compute resources. we explore
transfer learning based on model adaptation
as an approach for training asr models under
constrained gpu memory, throughput and
training data. we conduct several systematic
experiments adapting a wav2letter convolutional neural network originally trained for
english asr to the german language. we
show that this technique allows faster training
on consumer-grade resources while requiring
less training data in order to achieve the same
accuracy, thereby lowering the cost of training asr models in other languages. model
introspection revealed that small adaptations
to the network’s weights were sufficient for
good performance, especially for inner layers.