Sahar Jamal


Exploring Transfer Learning for Urdu Speech Synthesis
Sahar Jamal | Sadaf Abdul Rauf | Quratulain Majid
Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference

Neural methods in Text to Speech synthesis (TTS) have demonstrated momentous advancement in terms of the naturalness and intelligibility of the synthesized speech. In this paper we present neural speech synthesis system for Urdu language, a low resource language. The main challenge faced for this study was the non-availability of any publicly available Urdu speech synthesis corpora. Urdu speech corpus was created using audio books and synthetic speech generation. To leverage the low resource scenario we adopted transfer learning for our experiments where knowledge extracted is further used to train the model using a relatively smaller Urdu training data set. The results from this model show satisfactory results, though a good margin for improvement exists and we are working to improve it further.