  This paper addresses the observed performance gap between automatic speech
recognition (ASR) systems based on Long Short Term Memory (LSTM) neural
networks trained with the connectionist temporal classification (CTC) loss
function and systems based on hybrid Deep Neural Networks (DNNs) trained with
the cross entropy (CE) loss function on domains with limited data. We step
through a number of experiments that show incremental improvements on a
baseline EESEN toolkit based LSTM-CTC ASR system trained on the Librispeech
100hr (train-clean-100) corpus. Our results show that with effective
combination of data augmentation and regularization, a LSTM-CTC based system
can exceed the performance of a strong Kaldi based baseline trained on the same
data.
