The language diversity in India's education sector poses a significant
challenge, hindering inclusivity. Despite the democratization of knowledge
through online educational content, the dominance of English, as the internet's
lingua franca, limits accessibility, emphasizing the crucial need for
translation into Indian languages. Despite existing Speech-to-Speech Machine
Translation (SSMT) technologies, the lack of intonation in these systems gives
monotonous translations, leading to a loss of audience interest and
disengagement from the content. To address this, our paper introduces a dataset
with stress annotations in Indian English and also a Text-to-Speech (TTS)
architecture capable of incorporating stress into synthesized speech. This
dataset is used for training a stress detection model, which is then used in
the SSMT system for detecting stress in the source speech and transferring it
into the target language speech. The TTS architecture is based on FastPitch and
can modify the variances based on stressed words given. We present an Indian
English-to-Hindi SSMT system that can transfer stress and aim to enhance the
overall quality and engagement of educational content.

通过提供一个带有印度英语重读标记注释的数据集，并且使用能够将重读融入合成语音的文本转语音架构，本文介绍了一个能够检测源语音中的重读并将其转移到目标语言语音中的印度英语到印度语的语音机器翻译系统，旨在提高教育内容的整体质量和吸引力。

口语到口语机器翻译中应力转移尝试

Attempt Towards Stress Transfer in Speech-to-Speech Machine Translation

In this work, we present our deployment-ready Speech-to-Speech Machine
Translation (SSMT) system for English-Hindi, English-Marathi, and Hindi-Marathi
language pairs. We develop the SSMT system by cascading Automatic Speech
Recognition (ASR), Disfluency Correction (DC), Machine Translation (MT), and
Text-to-Speech Synthesis (TTS) models. We discuss the challenges faced during
the research and development stage and the scalable deployment of the SSMT
system as a publicly accessible web service. On the MT part of the pipeline
too, we create a Text-to-Text Machine Translation (TTMT) service in all six
translation directions involving English, Hindi, and Marathi. To mitigate data
scarcity, we develop a LaBSE-based corpus filtering tool to select high-quality
parallel sentences from a noisy pseudo-parallel corpus for training the TTMT
system. All the data used for training the SSMT and TTMT systems and the best
models are being made publicly available. Users of our system are (a) Govt. of
India in the context of its new education policy (NEP), (b) tourists who
criss-cross the multilingual landscape of India, (c) Indian Judiciary where a
leading cause of the pendency of cases (to the order of 10 million as on date)
is the translation of case papers, (d) farmers who need weather and price
information and so on. We also share the feedback received from various
stakeholders when our SSMT and TTMT systems were demonstrated in large public
events.

本文介绍了面向英 - 印度语、英 - 马拉缇语和印度 - 马拉缇语语言对的部署就绪的语音 - to 语音机器翻译（SSMT）系统，该系统通过级联自动语音识别（ASR）、行话校正（DC）、机器翻译（MT）和文本 - to 语音合成（TTS）模型进行开发。