  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.
