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        <title>Exploring Transfer Learning for Urdu Speech Synthesis</title>
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            <title>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</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Exploring Transfer Learning for Urdu Speech Synthesis
%A Jamal, Sahar
%A Abdul Rauf, Sadaf
%A Majid, Quratulain
%Y Ojha, Atul Kr.
%Y Ahmadi, Sina
%Y Liu, Chao-Hong
%Y McCrae, John P.
%S 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
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F jamal-etal-2022-exploring
%X 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.
%U https://aclanthology.org/2022.eurali-1.11
%P 70-74

Markdown (Informal)

[Exploring Transfer Learning for Urdu Speech Synthesis](https://aclanthology.org/2022.eurali-1.11) (Jamal et al., EURALI 2022)

• Exploring Transfer Learning for Urdu Speech Synthesis (Jamal et al., EURALI 2022)

ACL

• Sahar Jamal, Sadaf Abdul Rauf, and Quratulain Majid. 2022. Exploring Transfer Learning for Urdu Speech Synthesis. In 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, pages 70–74, Marseille, France. European Language Resources Association.