Exploring Samsung Research centers worldwide, we delve into the cutting-edge mobile AI experiences that Samsung is spearheading. Discover how Galaxy AI is empowering users to unlock their full potential. Galaxy AI has expanded its language support to include 16 languages. This means that more individuals can now enhance their language skills, even without an internet connection. The on-device translation feature, which includes Live Translate, Interpreter, and Note Assist, enables users to conveniently translate text in various languages. However, the process of AI language development may be of interest to you. In this series, we explore the obstacles encountered while working with mobile AI and the strategies we employed to overcome them. Our first destination is Indonesia, where we explore the fascinating process of teaching AI to speak a new language.
Samsung R&D Institute Indonesia (SRIN) suggests starting with setting goals. The foundation of great AI is high-quality, relevant data. The head of artificial intelligence at SRIN, Junaidillah Fadlil, recently added support for Bahasa Indonesia (the Indonesian language) to Galaxy AI, explaining that their team dove deep into understanding the linguistic needs and specific conditions of their country because every language demands a distinct manner to process this. “All processes for adding languages to Galaxy AI begin with us planning what information we need and can legally and ethically obtain,” the researchers explained, explaining that “local language development has to be led by insight and science.”
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 3rd 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.
