A diverse country with more than 203 million people embodying a wide range of cultures and traditions, Brazil uses Brazilian Portuguese as its official language. Meanwhile, 22 neighboring countries use Latin American Spanish.

Although Brazilian Portuguese and Latin American Spanish are widely spoken, intricate variations in both languages presented various challenges when teaching Galaxy AI to discern and distinguish regional differences. That’s why Samsung R&D Institute Brazil (SRBR) collaborated with Samsung experts from Mexico — as well as third-party partners such as the science and technology institutes SiDi and Sidia — to assemble a multidisciplinary and highly skilled team that could tackle the task.

Lower Barriers, Higher Understanding

The team used thousands of sources and a combination of machine learning and language processing tools to improve the AI model’s recognition of speech, written texts, and regional variations. But local jargon and names of famous figures — including sports teams, celebrities, and bands — vary widely between regions. Also, the same meaning can be expressed in many different words. While language models need localized data to gain a comprehensive understanding of the different languages to be translated, such variations inevitably present obstacles.

For example, swimming pool is “alberca” in Mexico — but it is “pileta” in Argentina, Paraguay and Uruguay. Meanwhile, in Colombia, Bolivia and Venezuela, swimming pool is “piscina”, which is also used in Brazil but with a slight tonal difference. And while Colombians might say “chévere” to refer to something cool, Mexicans instead say “padre.”

These differences represent huge challenges for AI language understanding and learning, but the team overcame them by building larger language models, refining processing tools – and collaborating across borders and time zones.

“We had to consider local slang and different ways of speaking before adapting and testing the model accordingly, which required close collaboration between the SRBR quality assurance (QA) team and development teams,” says Mateus Pedroso, Senior Manager and Head of Software Quality Lab at SRBR. “Since SRBR is located three hours ahead of the QA team in Mexico and 12 hours behind the management team in Korea, we had to create new communication channels and processes to align results and share progress. This multicultural collaboration generated a fiesta of ideas and solutions for Galaxy AI.”

Communicating Success

Samsung’s philosophy of open collaboration came to life during this regional project as it was an iterative process that leveraged evolving technology on a global scale. To overcome linguistic and cultural barriers, the SRBR team needed to collect and manage massive amounts of data — continually refining and improving upon audio and text sources.

The teams carved out key areas of responsibility to ensure everyone could benefit from the collective skill sets across the company’s Latin American offices. The SRBR development team served as the intermediate stakeholder of the project, receiving directions from Samsung’s headquarters and developing new updates to improve the AI model while carrying out tests for numerous use cases.

“The testing phase required extensive communication and collaboration with QA teams to optimize the user experience, and each adjustment needed further testing and review,” says Leandro Flores de Moura, Software Development Manager at SiDi. “The success of Galaxy AI’s language capabilities is built on communication and collaboration as much as it is on technical expertise” adds Nathan Castro, QA Test Developer at SiDi.

A Roadmap for Culture

What makes Galaxy AI particularly interesting for everyone involved is the fact that this wasn’t merely a language project. To them, language is a cultural guide that provides valuable insight into people’s heritage and identity.

“For SiDi’s QA team, this was an endeavor that will change the world by enabling cultures to come together and overcome the difficulty of communicating in different languages,” adds Estefanía Castro Suárez, Test Developer at SiDi. “Knowing we were part of this fills us with pride and motivation.”

“The way the SRBR team collaborated exemplifies what Galaxy AI sets out to achieve—making the world a smaller place through communicating, sharing and interacting with people, even those who speak different languages,” concludes Pedroso. “This capability will only grow as more languages come on board with Galaxy AI.”

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To learn more about Galaxy AI, visit: https://www.samsung.com/my/galaxy-ai

Samsung is pioneering premium mobile AI experiences. To learn how Galaxy AI is maximizing the potential of its users, we are visiting Samsung Research centers around the world. Now supporting 16 languages, Galaxy AI is enabling more people to expand their language capabilities, even when offline, thanks to on-device translation in features such as Live Translate, Interpreter, Note Assist and Browsing Assist. We recently visited Jordan to learn the complexities of developing an AI model for Arabic, a language with many dialects. This time, we’re going to Vietnam to explore how data is prepared to train AI models.

What is the difference between a ghost, grave and mother in Vietnamese? For a language spoken by 97 million people worldwide, very little. Each word translates to “ma,” “mả” and “má,” respectively — and can only be distinguished by tone. This illustrates how difficult it can be for AI models to learn a language, considering they cannot recognize firsthand the context and emotions of conversations nor the intentions of those speaking.

Samsung R&D Institute Vietnam (SRV) used finely refined data to help its AI model properly recognize even the most subtle differences in language.

The quality of data used directly affects the accuracy of automatic speech recognition (ASR), neural machine translation (NMT) and text-to-speech (TTS) — processes that help Galaxy AI features such as Live Translate, Interpreter, Chat Assist and Browsing Assist break down language barriers.

A Typhoon of Challenges

“Vietnamese is a complex and diverse language with rich expressions, many of which are challenging to capture,” says Ngô Hồng Thái, NMT lead at SRV. Of the 16 languages that Galaxy AI supports, Vietnamese was particularly difficult to develop.

“Personally, creating an AI model for Vietnamese was more daunting than our typhoons!” he adds before explaining the hurdles faced during the development process.

Vietnamese is a tonal language with six distinct tones. As evident in the “ma” example above, small nuances in vocalization can drastically alter the meanings of words. Therefore, a meticulous and detailed approach was necessary.

“When similar sounding words are broken down, one word consists of several short segments, or ‘frame sets’,” says Bui Ngoc Tung, ASR lead at SRV. “The AI model differentiates between the short audio frames of around 20 milliseconds to recognize what words correspond to a certain set of consecutive frames. As such, it is critical to put great effort into the early stages of the AI learning process.”

Furthermore, homophones and homonyms are common in Vietnamese. People can normally rely on context and nonverbal elements in conversations to differentiate between words that sound the same or are written the same but have different meanings. However, AI models need to be taught to accurately identify and differentiate between tones and similar words.

“This isn’t a straightforward task,” Thái explains. “Apart from the amount, the data needs to be accurate to ensure it is capable of recognizing the linguistic nuances that exist in Vietnamese.”

Rigorous Preparation

The data refinement process consists of three steps. First, the audio and text used to train the AI model must be reviewed and corrected. Then, this dataset goes through random checks for overall quality. Finally, the dataset is normalized and cleaned before use in training.

“We thoroughly performed a series of tests to check the accuracy of our dataset,” says Nguyen Manh Duy, TTS lead at SRV who oversees database creation. “We faced a number of unexpected problems including misspelled words in scripts and background noise or incorrect pronunciation during audio recordings. We spent significant time refining and improving our training data.”

In addition to the unique linguistic challenges in Vietnamese, there is a lack of universally accessible data compared to more widely spoken languages. “This is another reason why the data refinement stage is so important,” he adds. “Since we had limited sources, every piece of data had to be fully reliable. There was no margin for error.”

Moreover, the AI model for Vietnamese must consider both tonal and regional differences. To improve the AI model’s accuracy, the team collected vast amounts of data with Vietnam’s northern, central and southern accents — resulting in an enormous amount of information to refine and verify.

Continued Improvement

Developers at SRV completed the project after months of hard work, and Vietnamese became one of the first languages to be supported by Galaxy AI. Despite this success, the team is ceaselessly working to improve the Vietnamese Galaxy AI experience.

“We’re continuing to enhance the AI model by incorporating user feedback about the relevance of words and phrases in Galaxy AI,” says Tran Tuan Minh, leader of the AI language development project at SRV. “We have just taken our first steps into a more open world  —  and we have so much more to explore together.”

In the next episode of The Learning Curve, we will head to China to dig into how AI models are trained and fine-tuned.

Join Galaxy AI-Volution Squad Today!

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To learn more about Galaxy AI, visit: https://www.samsung.com/my/galaxy-ai

As Samsung’s continues to pioneer premium mobile AI experiences, we visit Samsung Research centers around the world to learn how Galaxy AI is enabling more users to maximize their potential. Galaxy AI now supports 16 languages, so more people can expand their language capabilities, even when offline, thanks to on-device translation in features such as Live Translate, Interpreter and Note Assist. But what does AI language development involve? This series examines the challenges of working with mobile AI and how we overcame them. First up, we head to Indonesia to learn where one begins teaching AI to speak a new language.

The first step is establishing targets, according to the team at Samsung R&D Institute Indonesia (SRIN). “Great AI begins good quality and relevant data. Each language demands a different way to process this, so we dive deep to understand the linguistic needs and the unique conditions of our country,” says Junaidillah Fadlil, head of AI at SRIN, whose team recently added Bahasa Indonesia (Indonesian language) support to Galaxy AI. “Local language development has to be led by insight and science, so every process for adding languages to Galaxy AI starts with us planning what information we need and can legally and ethically obtain.”

Galaxy AI features such as Live Translate perform three core processes: automatic speech recognition (ASR), neural machine translation (NMT) and text-to-speech (TTS). Each process needs a distinct set of information.

ASR, for instance, needs extensive recordings of speech in numerous environments, each paired with an accurate text transcription. Varying background noise levels help account for different environments. “It’s not enough just to add noises to recordings,” explains Muchlisin Adi Saputra, the team’s ASR lead. “In addition to the language data we obtained from authorized 3^rd party partners, we must go out into coffee shops or working environments to record our own voices. This allows us to authentically capture unique sounds from real life, like people calling out or the clattering of keyboards.”

The ever-changing nature of languages must also be considered. Saputra adds: “We need to keep up to date with the latest slang and how it is used, and mostly we find it on social media!”

Next, NMT requires translation training data. “Translating Bahasa Indonesia is challenging,” says Muhamad Faisal, the team’s NMT lead. “Its extensive use of contextual and implicit meanings relies on social and situational cues, so we need numerous translated texts that the AI could reference for new words, foreign words, proper nouns, and idioms – any information that helps AI understand the context and rules of communication.”

TTS then requires recordings that cover a range of voices and tones, with additional context on how parts of words sound in different circumstances. “Good voice recordings could do half the job and cover all the required phonemes (units of sound in speech) for the AI model,” adds Harits Abdurrohman, TTS lead. “If a voice actor did a great job in the earlier phase, the focus shifts to refining the AI model to clearly pronounce specific words.”

Stronger Together

It takes vast resources to plan for much data, and SRIN worked closely with linguistics experts. “This challenge requires creativity, resourcefulness and expertise in both Bahasa Indonesia and machine learning,” Fadlil reflects. “Samsung’s philosophy of open collaboration played a big part in getting the job done, as did our scale of operations and history of AI development.”

Working with other Samsung Research centers around the world, the SRIN team was able to quickly adopt best practices and overcome the complexities of establishing data targets. Furthermore, collaboration was good for advancing not only technology but also culture. When the SRIN team joined their counterparts in Bangalore, India, they observed the local fasting customs, creating deeper connections and expanding their understanding of different cultures.

For the team, Galaxy AI’s language expansion project took on a new significance. “We are particularly proud of our achievements here as this was our first AI project, and it won’t be our last as we continue to refine our models and improve the quality of output,” Fadlil concludes. “This expansion not only reflects our values of openness but also respects and incorporates our cultural identities through language.”

In the next episode of The Learning Curve, we will head to Samsung R&D Institute Jordan to speak to the team who led Galaxy AI’s Arabic language project. Tune in to learn about the complexities of building and training an AI model for a language with diverse dialects.