Meta AI Lunched first AI-Powered Speech Translation System For Written and Unwritten Language

Artificial Intelligence technology’s new language Artificial speech translation becomes a language of communication among those who speak a different language. In so many domains language made its way in itself.

Such as Global teaches conglomerates to use these share documents as well as audio conversations. Google announces its speech-to-speech Al translation model, “Translation Hub” with cloud translation APIs and AutoML translation.

AL-powered speech translation is based primarily on written language, till now more than 3500 languages can be translated into spoken and written. Hokkien is the first Al-powered translation system for a primarily oral language.

Meta Al, I confirm one more universal speech translation (UST) format. “Meta AI built the first speech translator that works for languages that are primarily spoken rather than written. We’re open-sourcing this so people can use it for more languages,” said Mark Zuckerberg, CEO of Meta. 

To establish this technology system, Meta’s AI has to face many challenges including data gathering, model design, and evaluation. Work becomes hard because of to increase in the number of languages. It makes it easy to speak without any barriers to so many languages.

Challenges Faced During Training

The major problem we faced is collect enough data for preparing the Hokkien translation system. There were many languages Spanish or English, and from English to Hokkien translation. The intermediate language we use is Mandarin t make a connection between human languages, first experiment we did is English to Mandarin text.

Meta AI-Powered speech
“They then added the paired sentences to the data used to train the AI model.”

Another major problem is regarding speech mining, fortunately, with the help of an encoder, we can encode the Hokkien language in another language.

“Our team first translated English or Hokkien speech to Mandarin text, and then translated it to Hokkien or English,” said Juan Pino, a researcher at Meta. “They then added the paired sentences to the data used to train the AI model.”

“For interactions, it will enable people from around the world to communicate with each other more fluidly, making the social graph more interconnected. In addition, using artificial speech translation for content allows you to easily localize content for consumption in multiple languages,” Falcon told. 

Soon, we can look forward to hosting podcasts, Reddit AMA, or Clubhouse-like experiences within the metaverse. Enabling those to be multicast in multiple languages expands the potential audience on a massive scale,” he said.

How does UST (Universal Speech Translator) Work?

The model S2UT used an implementation Meta previously pioneered. Meta AI used UnitY for two decodings first is for text and the second is for creat units. “Tâi-lô” is another system to automatically evaluate and translate Hokkines speech. The creative team also used the first Hokkien-English bidirectional speech-to-speech. To compute with another language Mate lunching Speech Matrix to connect more languages.

A future of Opportunities Speech Translation

Current Meta AI only keeps an eye to establish a speech-to-speech translation system. Yashar Behzadi, CEO of Synthesis AI stated that this technology can be improved more if the metaverse proved successful.

“To train robust AI models requires vast amounts of representative data. A significant bottleneck to building these AI models in the near future will be the privacy-compliant collection, curation, and labeling of training data,” he said.

“The inability to capture sufficiently diverse data may lead to bias, differentially impacting groups of people. Emerging synthetic voice and NLP technologies may play an important role in enabling more capable models.”

The main aim of Meta is to finish all problems related to language in the world and the metaverse is another language that is written or unwritten.

@MetaAI(2/3) Hokkien, one of ~3k primarily spoken languages, has no standard writing system and very few human translators, making it even more difficult to create training data for our models and difficult to rely on Hokkien transcripts.

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