  Deep learning mechanisms are prevailing approaches in recent days for the
various tasks in natural language processing, speech recognition, image
processing and many others. To leverage this we use deep learning based
mechanism specifically Bidirectional- Long Short-Term Memory (B-LSTM) for the
task of dialectic identification in Arabic and German broadcast speech and Long
Short-Term Memory (LSTM) for discriminating between similar Languages. Two
unique B-LSTM models are created using the Large-vocabulary Continuous Speech
Recognition (LVCSR) based lexical features and a fixed length of 400 per
utterance bottleneck features generated by i-vector framework. These models
were evaluated on the VarDial 2017 datasets for the tasks Arabic, German
dialect identification with dialects of Egyptian, Gulf, Levantine, North
African, and MSA for Arabic and Basel, Bern, Lucerne, and Zurich for German.
Also for the task of Discriminating between Similar Languages like Bosnian,
Croatian and Serbian. The B-LSTM model showed accuracy of 0.246 on lexical
features and accuracy of 0.577 bottleneck features of i-Vector framework.
