STRATA: Word Boundaries Phoneme Recognition From Continuous Urdu Speech using Transfer Learning, Attention, Data Augmentation
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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 the word boundaries with 92.2 techniques to further reduce the loss by 1.5 signals both in terms of generalization and accuracy. STRATA is able to achieve a Phoneme Error Rate of 16.5 for TIMIT dataset (English) and 11.5
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