End-to-End Speech Translation for Low-Resource Languages Using Weakly Labeled Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0199932" target="_blank" >RIV/00216305:26230/26:0199932 - isvavai.cz</a>

Result on the web

<a href="https://www.isca-archive.org/interspeech_2025/pothula25_interspeech.pdf" target="_blank" >https://www.isca-archive.org/interspeech_2025/pothula25_interspeech.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/interspeech.2025-2525" target="_blank" >10.21437/interspeech.2025-2525</a>

Result language

angličtina

Original language name

End-to-End Speech Translation for Low-Resource Languages Using Weakly Labeled Data

Original language description

The scarcity of high-quality annotated data presents a significant challenge in developing effective end-to-end speech-to-text translation (ST) systems, particularly for low-resource languages. This paper explores the hypothesis that weakly labeled data can be used to build ST models for low-resource language pairs. We constructed speech-to-text translation datasets with the help of bitext mining using state-of-the-art sentence encoders. We mined the multilingual Shrutilipi corpus to build Shrutilipi-anuvaad, a dataset comprising ST data for language pairs Bengali-Hindi, Malayalam-Hindi, Odia-Hindi, and Telugu-Hindi. We created multiple versions of training data with varying degrees of quality and quantity to investigate the effect of quality versus quantity of weakly labeled data on ST model performance. Results demonstrate that ST systems can be built using weakly labeled data, with performance comparable to massive multi-modal multilingual baselines such as SONAR and SeamlessM4T.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EH23_020%2F0008518" target="_blank" >EH23_020/0008518: Linguistics, Artificial Intelligence and Language and Speech Technologies: from Research to Applications</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Interspeech 2025

ISBN

—

ISSN

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e-ISSN

2958-1796

Number of pages

5

Pages from-to

41-45

Publisher name

ISCA

Place of publication

Rotterdam

Event location

Rotterdam

Event date

Aug 17, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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