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        <title>Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages</title>
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        <namePart>Jamaluddin</namePart>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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        <name type="personal">
            <namePart type="given">Selene</namePart>
            <namePart type="family">Baez Santamaria</namePart>
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        <name type="personal">
            <namePart type="given">Sai</namePart>
            <namePart type="given">Ashish</namePart>
            <namePart type="family">Somayajula</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Atsuki</namePart>
            <namePart type="family">Yamaguchi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Indian languages represent a highly multilingual and low-resource speech ecosystem, where the scarcity of high-quality parallel speech corpora significantly limits the development of speech-to-speech translation systems. Most existing approaches rely on cascaded pipelines that combine automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS). While effective, these cascaded systems often suffer from cumulative error propagation, increased latency, and higher computational complexity, particularly in low-resource Indian languages. To address these challenges, my doctoral work proposes a novel sequence-to-sequence direct speech translation framework capable of translating speech from one Indian language to another without relying on intermediate text representations. Recent advances in deep learning, however, indicate that direct speech translation architectures can surpass conventional cascaded systems in both efficiency and translation quality, motivating the design of our fully end-to-end solution. We aim to release an initial dataset comprising at least 120,000 real speech samples within a 6–12 month timeframe.</abstract>
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%0 Conference Proceedings
%T Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages
%Y Baez Santamaria, Selene
%Y Somayajula, Sai Ashish
%Y Yamaguchi, Atsuki
%A Jamaluddin
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-383-8
%F jamaluddin-2026-thesis
%X Indian languages represent a highly multilingual and low-resource speech ecosystem, where the scarcity of high-quality parallel speech corpora significantly limits the development of speech-to-speech translation systems. Most existing approaches rely on cascaded pipelines that combine automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS). While effective, these cascaded systems often suffer from cumulative error propagation, increased latency, and higher computational complexity, particularly in low-resource Indian languages. To address these challenges, my doctoral work proposes a novel sequence-to-sequence direct speech translation framework capable of translating speech from one Indian language to another without relying on intermediate text representations. Recent advances in deep learning, however, indicate that direct speech translation architectures can surpass conventional cascaded systems in both efficiency and translation quality, motivating the design of our fully end-to-end solution. We aim to release an initial dataset comprising at least 120,000 real speech samples within a 6–12 month timeframe.
%U https://aclanthology.org/2026.eacl-srw.41/
%P 535-543

Markdown (Informal)

[Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages](https://aclanthology.org/2026.eacl-srw.41/) (Jamaluddin, EACL 2026)

• Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages (Jamaluddin, EACL 2026)

ACL

• Jamaluddin. 2026. Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 535–543, Rabat, Morocco. Association for Computational Linguistics.