  We present the speech to text transcription system, called DARTS, for low
resource Egyptian Arabic dialect. We analyze the following; transfer learning
from high resource broadcast domain to low-resource dialectal domain and
semi-supervised learning where we use in-domain unlabeled audio data collected
from YouTube. Key features of our system are: A deep neural network acoustic
model that consists of a front end Convolutional Neural Network (CNN) followed
by several layers of Time Delayed Neural Network (TDNN) and Long-Short Term
Memory Recurrent Neural Network (LSTM); sequence discriminative training of the
acoustic model; n-gram and recurrent neural network language model for decoding
and N-best list rescoring. We show that a simple transfer learning method can
achieve good results. The results are further improved by using unlabeled data
from YouTube in a semi-supervised setup. Various systems are combined to give
the final system that achieves the lowest word error on on the community
standard Egyptian-Arabic speech dataset (MGB-3).
