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        <title>Speech-to-Speech Machine Translation for Dialectal Variations of Hindi</title>
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    <abstract>Hindi has many dialects and they are vital to India’s cultural and linguistics heritage. However, many of them have been largely overlooked in modern language technological advancements, primarily due to lack proper resources. In this study, we explore speech-to-speech machine translation (S2ST) for four Hindi dialects, i.e., Awadhi, Bhojpuri, Braj Bhasha, and Magahi. We adopt a cascaded S2ST pipeline comprising of three stages: Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS). We evaluate many recent large language models (LLMs) for dialect-to-Hindi and dialect-to-English translations in zero-shot, few-shot, and chain-of-thought setups. Our comparative analysis offers insights into the current capabilities and limitations of LLM-based approaches for low-resource dialectal S2ST in Indian context.</abstract>
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%0 Conference Proceedings
%T Speech-to-Speech Machine Translation for Dialectal Variations of Hindi
%A Sood, Sanmay
%A Rajput, Siddharth
%A Akhtar, Md Shad
%Y Nakazawa, Toshiaki
%Y Goto, Isao
%S Proceedings of the Twelfth Workshop on Asian Translation (WAT 2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-309-8
%F sood-etal-2025-speech
%X Hindi has many dialects and they are vital to India’s cultural and linguistics heritage. However, many of them have been largely overlooked in modern language technological advancements, primarily due to lack proper resources. In this study, we explore speech-to-speech machine translation (S2ST) for four Hindi dialects, i.e., Awadhi, Bhojpuri, Braj Bhasha, and Magahi. We adopt a cascaded S2ST pipeline comprising of three stages: Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS). We evaluate many recent large language models (LLMs) for dialect-to-Hindi and dialect-to-English translations in zero-shot, few-shot, and chain-of-thought setups. Our comparative analysis offers insights into the current capabilities and limitations of LLM-based approaches for low-resource dialectal S2ST in Indian context.
%U https://aclanthology.org/2025.wat-1.5/
%P 54-65

Markdown (Informal)

[Speech-to-Speech Machine Translation for Dialectal Variations of Hindi](https://aclanthology.org/2025.wat-1.5/) (Sood et al., WAT 2025)

• Speech-to-Speech Machine Translation for Dialectal Variations of Hindi (Sood et al., WAT 2025)

ACL

• Sanmay Sood, Siddharth Rajput, and Md Shad Akhtar. 2025. Speech-to-Speech Machine Translation for Dialectal Variations of Hindi. In Proceedings of the Twelfth Workshop on Asian Translation (WAT 2025), pages 54–65, Mumbai, India. Association for Computational Linguistics.