There’s a saying at the Samsung R&D Institute Poland (SRPOL): “A day at SRPOL lasts 96 hours”. It refers to the center’s global role as one of the largest and fastest-growing R&D centers in the region, often working across four different time zones. Sitting at the heart of Europe while covering many European and global markets, SRPOL has worked on automatic speech recognition, neural machine translation and text-to-speech models for more than 30 languages. When it came to bringing 10 languages to Galaxy AI, this expertise meant the team was well suited to seamlessly blend cultural perspectives with Samsung’s global technology.

SRPOL has years of experience in Natural Language Processing. What makes it unique is its adaptability to work on any language thanks to the passionate team and their tools, such as a crowdsourcing platform that enables fast and agile development.

“Collaboration across the continent means relentless data collection, annotation and research, which has become something we really enjoy,” says Kornel Jankowski, Head of Speech Decoding at SRPOL. “We’ve dealt with so many languages that our team developed universal, language-agnostic skills. When we’re asked to support a new language model, everybody’s attitude is: Oh wow, we get to learn another one, that’s going to be fun!”

A European Center for AI Language Development

Language is a cornerstone of culture and communication across Europe regardless of whether it’s incorporated into technology. However, it presents unique challenges for the team at SRPOL, who develop AI models for European languages.

“Each language and the culture that it is part of, comes with hurdles that make us reevaluate how we perceive a specific issue,” explains Adam Ros, Head of Artificial Intelligence at SRPOL. These hurdles include navigating the untranslatability of certain phrases and handling idiomatic expressions that may not have direct equivalents in other languages.

The team saw these challenges as an opportunity to make SRPOL a European center for AI language development. The biggest benefit of this is that it shortens the communication path between different departments and crucially, the decision-making path. Whether it is a matter of automatic speech recognition, neural machine translation or text-to-speech, teams could simply walk over to colleagues in Mobile Quality Assurance and efficiently solve problems together.

While this has helped, it hasn’t overcome all AI challenges. Inevitably, there are limitations in AI models when dealing with multiple European languages, such as translating without context or variations in intonation. However, the team saw these as an opportunity to keep learning and innovating.

“My team never stops at just one example when handling a new word or topic. Some European languages are harder than others,” adds Ros. If you’ve ever been to Spain, you know that Spanish is often spoken at blazing-fast speeds and we need to train AI well to handle that.”

Galaxy AI’s expansion required novel cross-continent collaboration, but the work soon grew beyond European borders. SRPOL supported the Jordan team’s efforts to teach Galaxy AI Arabic’s myriad of dialects, as well as the Brazil team’s work on Latin American languages.

The importance of language and cultural difference subtleties are all on the radar of SRPOL product developers because they can all be noticed by the target — the end users.

“There are subtle differences between European cultures that impact whether something feels natural to the end user. For example, people in some countries expect to read prices with the euro symbol (€), while others are accustomed to seeing it spelled out, e-u-r-o-s,” says Agata Maria Rozycka, Head of Voice Intelligence Research at SRPOL. “If this cultural nuance is not reflected translated text, the interface might seem less intuitive to a user. Implementing these micro-level insights into interface design can make technology feel more natural across diverse cultures.”

“The team has been remotely communicating and collaborating across different countries for many years, building up numerous effective communication channels,” says Marcin Mrugala, Head of Mobile Quality Assurance at SRPOL. “We were ready to do our part in enabling Galaxy AI to lower language barriers around the world.”

Technology for Bridging Cultures

Managing and integrating diverse linguistic and cultural insights is a challenging task, but it is essential for Samsung’s vision for Galaxy AI — lowering the barriers that divide people based on language and culture, and enabling them to create deeper connections.

“We’re not just building technology of the future, we’re building teams of the future too. Our best practices are designed to refine products based on differences across countries, but we fundamentally believe our similarities far outweigh our differences and our technology can unite cultures,” says Mrugala.

“Our goal is to bring people together, to make their lives easier, and to simplify their daily tasks. We’re seeing our families using the Voice Recorder in new ways, and we can now call our friends and different countries and talk with them in their own language. It is magical to see this change in the world and to be part of it. Galaxy AI brought SRPOL people together and now we are bringing together the world,” concludes Rozycka.

Hidden inside the Vellore Institute of Technology in Chennai, India, is a lab filled with futuristic audio equipment. One will find mannequins — known in the industry as head and torso simulators — as well as binaural microphones and hearing devices. They are stored in special chambers treated with an advanced sound absorption system, making this lab the first of its kind in India. Imagine such a facility is used to develop the latest high-end high fidelity (Hi-Fi) equipment.

This is where the Vellore Institute of Technology collaborates with Samsung to produce and develop data and insights that power the latest AI models for Galaxy AI’s language capabilities. The facility was developed as part of Samsung SEED (Students Ecosystem for Engineered Data) Labs — an initiative that enables university staff, students and interns in India to work on projects requested by Samsung since 2021. This is just one of several university programs funded by Samsung in which students have the opportunity to work on projects with technical experts from the company.

“As a student, I love being able to work on multiple projects with a well-known and respected company such as Samsung,” says Yashika Ilanchezhiyan, a Samsung SEED student. “I’m given the confidence to learn new skills in a practical way and feel like I’m making a real difference in current and future products.”

“This kind of collaboration is a win-win situation,” says Giridhar Jakki, Head of Language AI at Samsung R&D Institute India – Bangalore (SRI-B). “Thanks to our projects with universities, we are able to access additional expertise and custom datasets. Partnering universities receive investment, financial incentives and expert mentorship from Samsung as a result.”

Lowering Language Barriers

SRI-B has collaborated with teams around the world to develop AI language models for British, Indian and Australian English as well as Thai, Vietnamese and Indonesian. Recently, core engineers from other Samsung Research centers visited Bangalore, India — where the SRI-B team helped ramp up the technology to bring Vietnamese, Thai and Indonesian to Galaxy AI. SRI-B was therefore ideally positioned to develop the Hindi language for Galaxy AI.

“Every language has its challenges,” says Jakki. “But when you consider the end goal of bringing people the ability to communicate in other languages, it’s worth every ounce of effort. We couldn’t wait to bring Hindi to Galaxy AI.”

Developing the Hindi AI model wasn’t simple. The team had to ensure more than 20 regional dialects, tonal inflections, punctuation and colloquialisms were covered. Additionally, it is common for Hindi speakers to mix English words in their conversations. This required the team to carry out multiple rounds of AI model training with a combination of translated and transliterated data.

“Hindi has a complex phonetic structure that includes retroflex sounds — sounds made by curling the tongue back in the mouth — which are not present in many other languages,” says Jakki. “To build the speech synthesis element of the AI solution, we carefully reviewed data with native linguists to understand all the unique sounds and created a special set of phenomes to support specific dialects of the language.”

Collaborative efforts between Samsung and academic partners were instrumental in developing the AI language model that reflected the cultural nuances of the India’s regions. The Vellore Institute of Technology helped secure almost a million lines of segmented and curated audio data on conversational speech, words and commands. Data was a crucial component for a task as critical as incorporating the fourth most spoken language in the world into Galaxy AI. Working with universities ensured Samsung was using the highest quality data.

Global Connections Deliver Big Impacts

This project perfectly encapsulates Samsung’s philosophy of open collaboration and the company’s belief that sharing expertise and perspectives ensures meaningful innovation. In the case of SRI-B, this not only includes working with academia but also sharing insights and best practices with other Samsung research centers around the world.

“I’m extremely proud of what we’ve achieved with the help of our partners,” says Jakki. “AI innovation through collaboration is a big part of what we do. We will continue to better understand, collect and analyze language data so more people can have access to AI tools in the future.”

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.”

As Samsung 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, Note Assist and Browsing Assist. But what does AI language development involve? Last time, we visited Vietnam to learn about preparing the data that is used to train AI models. This time, we’re seeing how teams made Galaxy AI a unique offering for both the Chinese mainland and Hong Kong.

The rapid growth in AI tools that use large language models (LLM) has been seen worldwide, and China is no exception. With Baidu’s ERNIE Bot and Meitu’s MiracleVision emerging as popular choices in China, Samsung R&D Institute China partnered with both companies to help build Galaxy AI features for the country.

Samsung R&D Institute China in Guangzhou (SRC-G) and Beijing (SRC-B) worked to ensure Mandarin speakers in China had the same Galaxy AI experience as other users around the world, despite the back-end technology looking very different. The team took advantage of the dedicated resources of Chinese dialects from third-party partners and built a unique Galaxy AI solution for China.

“We have the advantage of blending global best practices with China’s local practices, as well as creating new features and constantly improving them through daily communication with Chinese consumers,” says Hairong Zhang, Software Innovation Group Leader at SRC-G. “With rich development experience from the Galaxy S24, I’m proud of how our team cooperated with local Chinese AI companies such as Baidu and Meitu to provide a solution that resonates in China.”

At the beginning, the teams had to acclimate to each other’s working styles and iron out the initial kinks of information asymmetry. Daijun Zhang, Head of SRC-B, established a task force to ensure the project followed the development schedule and moved quickly toward its goals.

Thanks to the Beijing team’s experience in generating large-scale models and successful collaboration with third-party partners, all the generative AI features were successfully launched in China. The result is a solution that has local relevance and market-specific features such as Touch to Search.

Expanding on Chinese To Develop for the Cantonese Dialect

Chinese for mainland China (Mandarin) arrived on Galaxy AI with the launch of the Galaxy S24 in January 2024. But the job for Samsung R&D Institute China was far from finished. The team was also tasked with developing the AI model for Chinese in Hong Kong (Cantonese), a dialect that builds on the work already carried out for Mandarin but brings an entirely new set of language features to address.

In developing for Cantonese, the China R&D team faced major cultural challenges that it needed to respond to in order to fully support localization for the market. The first cultural phenomenon is the two sets of systems for writing and speech. Hong Kong locals use grammar and expressions similar to Mandarin when writing but adopt a completely different colloquial grammar when communicating daily. Also, Cantonese has nine tones for pronunciation, whereas Mandarin has four.

Another cultural phenomenon is that the Cantonese dialect itself develops with the times. Add to that the fact that people often blend Cantonese and English into conversations, and it’s clear to see why it was complicated to create test cases and validate language packs.

“Cantonese is a very unique dialect that varies in different Cantonese-speaking regions,” says Jing Li, who leads the operation for testing the Cantonese AI solution. “Some of the slang, phrases, vocabulary and even the tones are varied from place to place. Therefore, we conducted a large amount of work in verifying the Hong Kong-specific data, as well as proofreading tens of thousands of relevant test cases.”

With these complexities in mind, SRC-G and SRC-B worked together to support a deep code mix using a mixture of Cantonese and English for speech recognition, simultaneously supporting both written and spoken expressions in machine translation and reflecting current pronunciations in speech synthesis.

Cultural Impact of Communication

When Galaxy AI launched the Chinese (Hong Kong) language option, the customer feedback showed that the hard work of the Samsung R&D team was justified.

For both the Chinese mainland and Hong Kong, Samsung’s Galaxy AI activities show the importance of a global brand having a local presence and expertise, as well as the power of open collaboration with other organizations. In Hong Kong, Cantonese is a key part of the cultural identity of those who live there. That’s why it was so important for the team to get the AI language model right.

“Language and communication are crucial in every region and in all walks of life,” says Henry Wat, Heads of Engineering Group at Samsung Electronics Hong Kong. “No matter the language, any tool that helps people communicate is invaluable. I believe our work is meaningful.”

In the next episode of The Learning Curve, we will head to Brazil to see how a team works across cultures and borders to bring Galaxy AI to more people.

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.”

A vital part of the data refinement process and the journey of taking AI data from good to great is the work of the Software Quality Engineering (SQE) team. The team plays an important role in testing and improving AI language data quality and they work closely with the AI language development project team to make it happen.

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.

Teaching a language to an AI model is a complex process, but what if it isn’t a singular language, but a collection of diverse dialects? That was the challenge faced by the team at Samsung R&D Institute Jordan (SRJO). While Arabic was added as a language option for Galaxy AI features such as Live Translate, the team had to cater to the various Arabic dialects that span the Middle East and North Africa, with each varying in pronunciation, vocabulary and grammar.

Arabic is one of the top six most widely spoken languages around the world, used daily by more than 400 million people.¹ The language is categorized into two forms: Fus’ha (Modern Standard Arabic) and Ammiya (the dialects of Arabic). Fus’ha is typically used in public and official events, as well as in news broadcasts, while Ammiya is more commonly used for day-to-day conversations. Over 20 countries use Arabic, and there are currently around 30 dialects in the region.

Unwritten Rules

Recognizing the variation presented by these dialects, the team at SRJO employed a range of techniques to discern and process the unique linguistic features inherent in each. This approach was crucial in ensuring that Galaxy AI could understand and respond in a way that accurately reflects the regional nuances.

“Unlike other languages, the pronunciation of the object in Arabic varies depending on the subject and verb in the sentence,” says Mohammad Hamdan, project leader of the Arabic language development team. “Our goal is to develop a model that understands all these dialects and can answer in standard Arabic.”

TTS is the component of Galaxy AI’s Live Translate feature that lets users interact with speakers of different languages by translating spoken words into written text, and then vocally reproducing them. The TTS team faced a unique challenge, caused by the quirk of working with Arabic.

Arabic uses diacritics, which are guides for the pronunciation of words in some contexts, such as religious texts, poetry and books for language learners. Diacritics are widely understood by native speakers but absent in everyday writing. This makes it difficult for a machine to convert raw text into phonemes, the basic units of sound that are the building blocks of speech.

“There is a shortage of high-quality and reliable datasets that accurately represent how diacritics are correctly used,” explains Haweeleh. “We had to design a neural model that can predict and restore those missing diacritics with high accuracy.”

Neural models work similarly to human brains. To predict diacritics, a model needs to study lots of Arabic text, learn the language’s rules and understand how words are used in different contexts. For instance, the pronunciation of a word can vary greatly depending on the action or gender it describes. Extensive training from the team was the key to enhancing the Arabic TTS model’s accuracy.

Enhancing Understanding

The SRJO team also had to collect diverse audio recordings of the dialects from various sources, which had to be transcribed, focusing on unique sounds, words and phrases. “We assembled a team of native speakers in the dialects who were well-versed in the nuances and variations,” says Ayah Hasan, whose team was responsible for database creation. “They listened to the recordings and manually converted the spoken words into text.”

This work was crucial for enhancing the Automatic Speech Recognition (ASR) process so that Galaxy AI could handle the rich tapestry of Arabic dialects. ASR is pivotal in enabling Galaxy AI’s real-time understanding and response capabilities.

“Building an ASR system that supports multiple dialects in a single model is a complex undertaking,” says Mohammad Hamdan, ASR lead for the project. “It demands a thorough understanding of the language’s intricacies, careful data selection and advanced modeling techniques.”

The Culmination of Innovation

After months of planning, building and testing, the team was ready to release Arabic as a language option for Galaxy AI, enabling many more people to communicate across borders. This single team has made Galaxy AI services accessible to Arabic speakers, lowering the language and cultural barriers between them and people all over the world. In doing so, they have established new best practices that can be rolled out globally. This success is only the beginning: the team continues to refine their models and enhance the quality of Galaxy AI’s language capabilities.

In the next episode, we go to Vietnam to see how the team makes language data better. Plus, what does it take to train an effective AI model?

Arabic is just one part of the languages and dialects newly supported by Galaxy AI and available for download from the Settings app. Galaxy AI’s language features such as Live Translate and Interpreter are available on Galaxy devices running Samsung’s One UI 6.1 update.²

¹ UNESCO, World Arabic Language Day 2023, https://www.unesco.org/en/world-arabic-language-day
² One UI 6.1 was first released on Galaxy S24 series devices with a wider roll out to other Galaxy devices including S23 series, S23 FE, S22 series, S21 series, Z Fold5, Z Fold4, Z Fold3, Z Flip5, Z Flip4, Z Flip3, Tab S9 series and Tab S8 series

“Committed to democratizing mobile AI for all, Galaxy AI’s language expansion this year will allow even more Galaxy users to communicate beyond language barriers on a scale that is completely unique to Samsung,” said TM Roh, President and Head of Mobile eXperience Business at Samsung Electronics. “We will continue to innovate our technology and pioneer premium mobile AI experiences so that even more users are equipped with the right tools to unleash their unlimited potential.”

Galaxy AI Feature Descriptions

• Live Translate produces two-way, real-time voice and text translations of phone calls, making it easier than ever to book reservations while traveling or chat with your grandparents in their native language.
• Interpreter can instantly translate live conversations through a user-friendly split-screen view, allowing people standing opposite each other to read a text translation of what the other person is saying.
• Chat Assist can help perfect conversational tones by generating context-aware suggestions to ensure communication sounds as it was intended, whether it’s a polite message to a coworker or a short catchy phrase for a social media caption.
• Note Assist can create AI-generated summaries, pre-formatted templates and cover pages, elevating your day-to-day productivity.
• Transcript Assist uses AI and Speech-to-Text technology to transcribe, summarize and even translate voice recordings.
• Browsing Assist helps you stay up to speed on what’s happening in the world while saving time by generating concise summaries of news articles or web pages.

Starting in April and rolling out over the upcoming months, the newly supported Galaxy AI languages and dialects will each be available for download as a language pack from the Settings app.

For more information about Galaxy AI, please visit: Samsung Newsroom, Samsungmobilepress.com or Samsung.com.

¹ Supported languages include Chinese, English (India, United Kingdom, United States), French, German, Hindi, Italian, Japanese, Korean, Polish, Portuguese (Brazil), Spanish (Mexico, Spain, United States), Thai and Vietnamese.