  Phoneme recognition is a largely unsolved problem in NLP, especially for
low-resource languages like Urdu. The systems that try to extract the phonemes
from audio speech require hand-labeled phonetic transcriptions. This requires
expert linguists to annotate speech data with its relevant phonetic
representation which is both an expensive and a tedious task. In this paper, we
propose STRATA, a framework for supervised phoneme recognition that overcomes
the data scarcity issue for low resource languages using a seq2seq neural
architecture integrated with transfer learning, attention mechanism, and data
augmentation. STRATA employs transfer learning to reduce the network loss in
half. It uses attention mechanism for word boundaries and frame alignment
detection which further reduces the network loss by 4% and is able to identify
the word boundaries with 92.2% accuracy. STRATA uses various data augmentation
techniques to further reduce the loss by 1.5% and is more robust towards new
signals both in terms of generalization and accuracy. STRATA is able to achieve
a Phoneme Error Rate of 16.5% and improves upon the state of the art by 1.1%
for TIMIT dataset (English) and 11.5% for CSaLT dataset (Urdu).
