<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languages

Name: Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languages
Keywords: FOS: Computer and information sciences, Computer Science - Computation and Language, Computer Vision and Pattern Recognition (cs.CV), I.5.4, Computer Science - Computer Vision and Pattern Recognition, Computation and Language (cs.CL)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2025Publisher:arXiv

Authors: Pathak, Utkarsh; Gunda, Chandra Sai Krishna; Prakash, Anusha; Agarwal, Keshav; Murthy, Hema A.;

doi: 10.48550/arxiv.2506.03884

arXiv: http://arxiv.org/abs/2506.03884

Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languages

- Summary
- Subjects
- Metrics

Abstract

Text-to-speech (TTS) systems typically require high-quality studio data and accurate transcriptions for training. India has 1369 languages, with 22 official using 13 scripts. Training a TTS system for all these languages, most of which have no digital resources, seems a Herculean task. Our work focuses on zero-shot synthesis, particularly for languages whose scripts and phonotactics come from different families. The novelty of our work is in the augmentation of a shared phone representation and modifying the text parsing rules to match the phonotactics of the target language, thus reducing the synthesiser overhead and enabling rapid adaptation. Intelligible and natural speech was generated for Sanskrit, Maharashtrian and Canara Konkani, Maithili and Kurukh by leveraging linguistic connections across languages with suitable synthesisers. Evaluations confirm the effectiveness of this approach, highlighting its potential to expand speech technology access for under-represented languages.

Accepted at INTERSPEECH 2025

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, Computer Vision and Pattern Recognition (cs.CV), I.5.4, Computer Science - Computer Vision and Pattern Recognition, Computation and Language (cs.CL)

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Found an issue? Give us feedback

Average

Green