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SSMT Notebooks for NASSCOM Workshop on Speech To Speech Machine Translation

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NASSCOM - The NLP Week (Day 2)

SSMT Notebooks for NASSCOM Workshop on Speech To Speech Machine Translation

About

India’s cultural diversity can be gauged by the fact that we transact in 22 official languages and converse in thousands of dialects. Advanced AI/ML capabilities applied to NLP can spur seamless integration of the social and business ecosystem in India by mending language barriers. With over 700 million internet subscribers, there is a rapidly growing demand for understanding India’s vernacular languages for myriad functions in areas such as multilingual customer service, search applications in native language and virtual agents in vernacular languages.

Natural Language Processing (NLP) has the capability to enable wider reach of information aided by Internet explosion across the country. Initiatives like the National Language Translation Mission by Govt of India are aiming to create a ‘voice-based internet’ and have Indian Language technology systems and products deployed in the field with the help of start-ups. This will result in development of the next generation 'conversational' apps & websites.

The forthcoming edition of NASSCOM’s AI Parley will bring together on a series of expert programs focused on NLP for India. The week-long conference will emphasize 4 key segments –

Day 2 - Segment - Developing a high quality speech to speech machine translation (SSMT)

The session will help developers comprehend the workflow associated with developing speech to speech models. (2:00PM-6:00PM)

Speakers

  • Dr. Preethi Jyothi , Assistant Professor of Computer Science and Engineering Department at IIT Bombay
  • Dr. Pushpak Bhattacharyya, Professor of Computer Science and Engineering Department at IIT Bombay

Assisting Students

  • Jyotsana Khatri
  • Nikhil Saini
  • Vineet Bhatt
  • Aakash Banerjee

Time

Apr 6, 2021 02:00 PM in India

Main Event Webpage

https://nasscom.in/nlp-week/

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SSMT Notebooks for NASSCOM Workshop on Speech To Speech Machine Translation

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  • Jupyter Notebook 56.4%
  • Python 43.6%