Slator.com is leading source of analysis and research for the global translation, localization, and language technology industry. We host SlatorCon, the language industry’s foremost executive conference. We publish SlatorPod, the weekly language industry podcast.
Florian and Esther discuss the language industry news of the week, breaking down Slator’s 2025 Language Service Provider Index (LSPI), which features nearly 300 LSPs and reports 6.6% combined growth in 2024 revenues, totaling USD 8.4bn.
Florian touches on a surprise USD 10m donation from private equity executive Mario Giannini to launch a new MA translation and interpreting program at California State University, Long Beach. The duo talks about McKinsey’s State of AI report, which continues to classify translators as AI-related roles and shows that hiring them has become slightly easier.
In Esther’s M&A corner, TransPerfect announced two acquisitions, Technicolor Games and Blu Digital Group, further expanding its presence in gaming and media localization. In Israel, BlueLion and GATS merged to form TransNarrative, and Brazilian providers Korn Translations and Zaum Langs joined forces under the Idlewild Burg group.
Meanwhile, in funding, Teleperformance invested USD 13m in Sanas, a startup offering real-time accent translation for call centers to improve global communication. Lingo.dev raised USD 4.2m, while Dubformer secured USD 3.6m to develop the ‘Photoshop of AI dubbing’.
Florian shares insights from Slator’s 2025 Localization Buyer Survey, which found that over half of buyers want strategic AI support from vendors and many cite inefficient automation as a key challenge.
A Pinch, a Twitch, and Everything in Between: Pinch’s Christian Safka and Twitch’s Susan Maria Howard were among the top language industry leaders who joined hundreds of attendees on March 18, 2025, for the first SlatorCon Remote conference of the year.
Kicking off the day’s events, Slator’s Head of Advisory, Esther Bond, welcomed attendees and invited Managing Director Florian Faes to share the latest findings and insights in his highly anticipated 'industry health check.
In his presentation, Faes began by reflecting on the challenges of 2024. He discussed data from Slator’s 2025 Language Service Provider Index (LSPI) and highlighted the growth of interpreting-focused companies, contrasted with the struggles faced by small, undifferentiated agencies and the rapid rise of language AI, driven by companies like ElevenLabs and DeepL.
Faes also highlighted key findings from Slator’s 2025 Localization Buyer Survey, including the challenges buyers face in implementing AI and the growing need for AI partners to address inefficiencies. He also noted the mixed outlook for the industry in the year ahead.
LLMs Are Just the Beginning
The first expert presentation was delivered by Sara Papi, a Postdoctoral Researcher at the Fondazione Bruno Kessler, who discussed the current state of research in simultaneous speech-to-text translation.
Papi highlighted discrepancies between the original definition and current practices in the speech translation field, identified through a review of expert literature. She specifically pointed out issues related to the use of pre-segmented speech and inconsistencies in terminology.
Slator’s Head of Research, Anna Wyndham, moderated the first panel of the day, featuring Simone Bohnenberger-Rich, Chief Product Officer at Phrase; Simon Koranter, Head of Global Production & Engineering at Compass Languages; and Matteo Nonne, Localization Program Manager at On.
The panelists discussed the evolving role of generative AI in localization, highlighting its shift from initial experimentation to scalable solutions that drive growth. They shared insights on how AI is transforming localization from a cost center into a strategic function by enabling customized, context-aware content adaptation and addressing challenges related to return on investment (ROI) and stakeholder expectations.
Slator’s Alex Edwards, Senior Research Analyst, moderated another panel discussion focused on the adoption of large language models (LLMs) for AI translation in enterprise workflows. Panelists Manuel Herranz, CEO of Pangeanic, and Bruno Bitter, CEO of Blackbird.io, explored whether LLMs truly represent the state of the art.
Herranz and Bitter emphasized that middleware and techniques like Retrieval-Augmented Generation (RAG) are more advanced, and highlighted the importance of fine-tuning smaller, domain-specific models. They also discussed the role of orchestration technology in effectively managing a range of AI tools.
In his presentation, Supertext’s CEO Samuel Läubli echoed insights shared by other speakers, emphasizing that LLMs generate fluent texts by considering broader context. He explored the implications of an AI-first era for translation, the rise of smaller competitive players, and the continued importance of human expertise.
Läubli highlighted that the new Supertext resulted from a 2024 merger between LSP Supertext and AI translation company Textshuttle. He remarked, “I’ve been working in this field for 10 years now, but I haven’t seen a system or AI agent that can guarantee a correct translation — and I’m quite sure I won’t see it in the next 10 years.”
Teresa Toronjo, Localization Manager at Malt, discussed collaboration within leaner localization teams, stressing the importance of diverse partnerships, scalable processes, and maintaining quality consistency with cost-effectiveness guided by experts.
If you missed SlatorCon Remote March 2025 in real-time, recordings will be available soon through our Pro and Enterprise plans.
Joe Corkery, MD, CEO and Co-Founder of Jaide Health, joins SlatorPod to discuss how Jaide Health is driving medical interpreting and translation with AI, bridging communication gaps for limited English proficiency (LEP) patients and improving healthcare accessibility.
With a background in computer science, medicine, and AI product leadership at Google, Joe co-founded Jaide Health with Julie Wilner, RN, in 2023 to address a long-standing need for real-time, interactive communication for the LEP patient population.
Unlike older machine translation models, which worked sentence by sentence without context, Joe shares how generative AI can maintain coherence, track gender references, and infer meaning from prior context — crucial in medical settings.
The CEO remains pragmatic about Trump’s executive order designating English as the US’s official language and revoking previous language access mandates. He argues that such policies will not change the healthcare industry’s commitment to multilingual patient care but may push hospitals to seek more cost-effective solutions — potentially accelerating AI adoption.
Looking ahead, Jaide Health is focusing on expanding into document translation, particularly for discharge instructions and patient portal messaging, areas where current solutions are slow or impractical.
Slator’s 30-page 2025 Localization Buyer Survey provides key insights from enterprise buyers of translation and localization services. The survey of 50 enterprise buyers uncovers the challenges buyers expect to face in 2025 and assesses the extent to which they have extracted value from language AI to date.
Slator 2025 Localization Buyer Survey
The findings offer valuable insights for both buyers and providers. For enterprise buyers, the report serves as a benchmarking tool — buyers can compare their strategies on budgets, AI adoption, and operational challenges with industry peers.
For language service providers (LSPs) and translation management system (TMS) providers, the results provide direct insights into where buyers perceive gaps in support. The survey responses shed light on buyer expectations and priorities, helping inform service development, technology investment, and client engagement strategies.
Respondents represented a diverse range of industries, offering a broad perspective on localization strategies, operational hurdles, and technology-driven shifts.
AI’s impact on localization budgets was a key theme. The survey explored how buyers expect AI-driven efficiencies to influence spending — whether cost savings will reduce budget, be reinvested, or reallocated. The survey also assessed how successfully buyers have integrated AI to date, tangible value realized, and common barriers to realizing AI value.
The survey looks at changes in per unit base rates for translation over the past year and expectations for pricing in the next 12 months. These insights offer a view of how pricing strategies may evolve, allowing LSPs to anticipate pricing pressures and align with buyer expectations.
Operational challenges and inefficiencies were another key focus. Buyers identified their greatest cost inefficiencies and barriers to achieving Localization Buyer Survey goals. Open-text responses were categorized, revealing 10 common inefficiencies and five key challenges for 2025 — highlighting persistent hurdles from technological integration to internal complexity.
The survey also explored buyer expectations for AI implementation, particularly the role of LSPs. Buyers indicated whether they seek strategic guidance, hands-on support, or fully integrated AI solutions. The results show that most buyers view LSPs as strategic partners, expecting proactive support to scale AI solutions and optimize workflows.
Finally, the survey looked into buyer expectations for AI integration within the TMS. The results show that almost all buyers now see AI capabilities as a baseline requirement, with many expecting customizable AI workflows and integrated machine translation.
Ultimately, these insights aim to help stakeholders navigate the changing localization landscape in 2025 — enabling buyers to benchmark strategies, optimize workflows, and address operational challenges, while supporting LSPs and TMS providers in adapting services, aligning with buyer expectations, and responding effectively to emerging industry shifts.
An article on the Android Authority site from March 2025 states that the instant translation function could soon be available as a permanent feature within “Circle to Search.”
Android’s Instant Translation Gets Enhanced in ‘Circle to Search’
If enabled, users will be able to translate whatever they select on the screen using Circle to Search on the screen into a device’s supported languages (this varies by device brand and model) without navigating out of the search function.
Back in March 2024, Google announced the rollout of Language Translation Technology in the “Circle to Search” function for Android, which allows users to circle or tap text or an object on their mobile device screen and automatically search for it without switching apps.
So far, users have been able to translate things like menus on photos or PDFs by long-pressing the home button or navigation bar, and then tapping the translate icon.
Google’s Android XR, the operating system introduced in December 2024 and enhanced by Gemini 2.0, is slated to support instant translation technology as a regular feature on more than mobile devices. The function can be expected on watches, glasses, TVs, cars, and more, along with other AI-powered features like directions and message summaries.
At the time of writing, Google states that the Translation Management Systems feature is available in over 100 languages on its own Pixel smartphone. “If you want to chat with your cousin in Spain or translate a menu at a restaurant online, Circle to Search can help, no matter where the text appears – in a PDF, video, web page, or chat,” says the marketing copy in the Google Store.
In a February 20, 2025paper, researchers Danni Liu and Jan Niehues from the Karlsruhe Institute of Technology proposed a way to improve how large language models (LLMs) perform across different languages.
New Research Explores How to Boost Large Language Models’ Multilingual Performance
They explained that LLMs like Llama 3 and Qwen 2.5, show strong performance in tasks like machine translation (MT) but often struggle with low-resource languages due to limited available data. Current fine-tuning processes do not effectively bridge the performance gaps across diverse languages, making it difficult for models to generalize effectively beyond high-resource settings.
The researchers focus on leveraging the middle layers of LLMs to enable better cross-lingual transfer across multiple tasks, including MT.
LLMs consist of multiple layers. The early (or bottom) layers handle basic patterns like individual words, while the final (or top) layers focus on producing a response. The middle layers play a key role in capturing the deeper meaning of sentences and how different words relate to each other.
Liu and Niehues found that these middle layers “exhibit the strongest potential for cross-lingual alignment,” meaning they help ensure that words and phrases with similar meanings are represented in a comparable way across languages. Strengthening this alignment helps the model transfer knowledge between languages more effectively.
By extracting embeddings (i.e., representations of text in vector form) from the model’s middle layers and adjusting them so that equivalent concepts are closer together across languages, the researchers aim to improve the model’s ability to understand and generate text in multiple languages.
Alternating Training Strategy
Rather than relying solely on task-specific fine-tuning, they introduce an “alternating training strategy” that switches between task-specific fine-tuning (e.g., for translation) and alignment training. Specifically, an additional step — middle-layer alignment — is integrated into the fine-tuning process to ensure that the representations learned in one language are more transferable to others.
Tests showed that this method improved both translation accuracy and performance across both high-resource and low-resource languages. Liu and Niehues noted that the models were also able to generalize their performance to languages not included in the initial alignment training.
One significant advantage of this method is its modular nature: “task-specific and alignment modules trained separately can be combined post-hoc to improve transfer performance” without requiring full model retraining. This makes it possible to improve existing models with enhanced multilingual capabilities while avoiding the high computational costs of retraining from scratch.
Additionally, this approach is faster and more cost-effective since “a few hundreds of parallel sentences as alignment data are sufficient.”
The researchers have made the code available on GitHub, allowing others to implement and test their approach.
The discussion highlighted that language access has long been a key part of US policy, particularly in healthcare, education, and legal services. Dipak pointed out that eliminating language services would create inefficiencies, making it harder for medical professionals to provide accurate care.
CEOs React as Trump Declares English the Sole Official Language of the US
Peter emphasized the broader uncertainty the order creates as many organizations rely on federal funding for language services, and a lack of clear guidance could lead to reduced support in schools, courts, and public services.
Both CEOs acknowledged that while this order presents challenges, the language services industry has historically adapted to change. Dipak suggested that financial pressures may push the industry to innovate, potentially accelerating AI adoption in interpreting.
CEOs React as Trump Declares English the Sole Official Language of the US
While the long-term impact remains unclear, the consensus is that language access will persist — driven by business needs and market demand.
A new Executive Order published on March 1, 2025, by the US White House designates English as the only official language of the United States and revokes Executive Order 13166, “Improving Access to Services for Persons with Limited English Proficiency” (LEP), signed in 2000 during the Clinton administration.
Trump Makes English the Only Official Language of the US, Revokes Clinton Language Access Order
The new order states that it is in the country’s best interest for the federal government to designate only one official language. It also argues that “Establishing English as the official language will not only streamline communication but also reinforce shared national values, and create a more cohesive and efficient society.”
The order also specifies that agency heads, defined as “the highest-ranking official of an agency,” are “not required to amend, remove, or otherwise stop production of documents, products, or other services prepared or offered in languages other than English.”
While the text states that this Executive Order does not create new legal rights or benefits, and that it should be implemented subject to existing laws, the Trump administration has instructed the Attorney General to update policy guidance in line with the new official language designation.
A Shift in Language Access Policy?
Revoked order 13166 required federal agencies to examine their services and identify language assistance needs for LEP populations, mandating the development and implementation of systems to meet those needs.
Language access guidelines under 13166 aligned with Title VI of the Civil Rights Act of 1964, a federal law that prohibits discrimination on the basis of multiple criteria, including national origin (and by association, language). It expressly mandated recipients of federal financial assistance, such as healthcare providers and educational institutions, to offer language assistance to LEP applicants and beneficiaries.
In 2003, during George W. Bush’s administration, the LEP.gov website was launched to help federal agencies, advocates, and individuals access related information and services. And for a couple of decades, including during the previous Trump term (2017–2021), the US Department of Justice (DOJ) provided policy guidance to assist federal agencies for compliance.
Still standing, and not mentioned in the latest Executive Order, is the one formulated during the Biden administration: Executive Order 14091 (2023), “Further Advancing Racial Equity and Support for Underserved Communities Through the Federal Government.”
Executive Order 14091 instructs agencies to consider opportunities to “improve accessibility for people with disabilities and improve language access services to ensure that all communities can engage with agencies’ respective civil rights offices.”
At the time of writing, both the LEP.gov site and the US Commission on Civil Rights’ Limited English Proficiency Plan remain online.
The researchers say that the financial domain presents unique challenges for MT due to its reliance on precise terminology and strict formatting rules. They describe it as “an interesting use-case for MT” since key terms often shift meaning depending on context.
For example, the French word couverture means blanket in a general setting but hedge in financial texts. Such nuances are difficult to capture without larger translation units.
Despite strong research interest in document-level MT, specialized test sets remain scarce, the researchers note. Most datasets focus on general topics rather than domains such as legal and financial translation.
Given that many financial documents “contain an explicit definition of terms used for the mentioned entities that must be respected throughout the document,” they argue that document-level evaluation is essential.
DOLFIN allows researchers to assess how well MT models translate longer texts while maintaining context.
Unlike traditional test sets that rely on sentence-level alignment, DOLFIN structures data into aligned sections, enabling the evaluation of broader linguistic challenges, such as information reorganization, terminology consistency, and formatting accuracy.
Context-Sensitive
To build the dataset, they sourced parallel documents from Fundinfo, a provider of investment fund data, and extracted and aligned financial sections rather than individual sentences. The dataset covers English-French, English-German, English-Spanish, English-Italian, and French-Spanish, with an average of 1,950 segments per language pair.
The goal, according to the researchers, was to develop “a test set rich in context-sensitive phenomena to challenge MT models.”
To assess the usefulness of DOLFIN, the researchers evaluated large language models (LLMs) including GPT-4o, Llama-3-70b, and their smaller counterparts. They tested these models in two settings: translating sentence by sentence versus translating full document sections.
They found that DOLFIN effectively distinguishes between context-aware and context-agnostic models, while also exposing model weaknesses in financial translation.
Larger models benefited from more context, producing more accurate and consistent translations, while smaller models often struggled. “For some segments, the generation enters a downhill, and with every token, the model’s predictions get worse,” the researchers observed, describing how smaller LLMs failed to maintain coherence over longer passages.
DOLFIN also reveals persistent weaknesses in financial MT, particularly in formatting and terminology consistency. Many models failed to properly localize currency formats, defaulting to English-style notation instead of adapting to European conventions.
The dataset is publicly available on Hugging Face.
Authors: Mariam Nakhlé, Marco Dinarelli, Raheel Qader, Emmanuelle Esperança-Rodier, and Hervé Blanchon
How do US actor Sylvester Stallone, France’s minister for gender equality Aurore Bergé, and a multilingual/multibillion voice AI company collide in a tense drama? Since early January 2024, multiple online media sources have highlighted a clash that began withnewsthat “Armor,” a film starring Stallone, would feature AI dubbing.
For 50 years, Alain Dorval was the familiar voice of Stallone in French-dubbed films, but he passed away in February 2024. Minister Bergé happens to be Dorval’s daughter. Enter ElevenLabs, which in January 2024 reached a USD 3bn valuation, and found itself at the center of a weeks-long controversy over the cloning of Dorval’s voice.
Bergé publicly opposed (article in French) the use of her father’s digitally recreated voice, despite acknowledging a prior agreement to a test. “It was just a trial run, with an agreement strictly guaranteeing that my mother and I would have final approval before any use or publication. And that nothing could be done without our consent.”
According to Variety, which has followed the story since the partnership around “Armor” between Lumiere Ventures and ElevenLabs came to light, Bergé’s move galvanized the French actors’ guild (FIA, in French).
FIA’s representative, Jimmy Shuman, called the voice cloning attempt a “provocation” in the Variety article. That is because the union is in the midst of “negotiating agreements on limits for artificial intelligence and dubbing.”
The controversy over Stallone’s French voice underscores the potential for AI to displace voice actors, often celebrities in their own right across Europe.
ElevenLabs CEO, Mati Staniszewski, told Variety that “Recreating Alain Dorval’s voice is a chance to show how technology can honor tradition while creating new possibilities in film production.”
Like their US counterparts after a few notable actions, voice-over artists in several European countries are taking a proactive stance through their unions, including AI clauses in their contracts to restrict AI voice use to specific projects or outright banning work for studios that do not offer adequate protections.
Per the latest Variety article on the subject, voice actor Michel Vigné will be the voice of Stallone for the French release. According to IMDB, Vigné has already voiced Stallone in French in the past.
The larger issue remains: the film industry acknowledges that AI voice cloning technology is rapidly advancing and the drama around Armor’s French dubs serves as a symbol of things to come in Europe and beyond.
One decision that perhaps many voice actors will need to grapple with is whether they want their voice to be immortalized with AI or simply be replaced by it or by another actor.
Bryan Forrester, Co-founder and CEO of Boostlingo, returns to SlatorPod for round 2 to talk about the company’s growth, the US interpreting market, and the evolving role of AI.
Bryan explains howBoostlingobalances innovation with practicality, ensuring that new features align with customer needs. He highlights the company’s three-pronged strategy: retaining existing customers, enabling growth, and making long-term bets on emerging trends.
While tools like real-time captions and transcription enhance efficiency, Bryan stresses that AI alone cannot replace human interpreters in complex industries like healthcare. He highlights privacy, compliance, and the nuanced expertise of human interpreters as critical factors, positioning AI as a supportive tool rather than a replacement.
https://youtu.be/fMNcJ5EV2zk
Bryan discusses market dynamics and regulatory changes, including how those under the new US administration could influence language access demand, particularly in areas like healthcare and public services.
He describes Boostlingo’s strategy of leveraging third-party AI models, optimizing them with proprietary data, and rigorously testing to ensure quality and reliability. Looking ahead, Boostlingo plans to expand internationally and integrate AI ethically and effectively into its offerings, guided by its newly formedAI Advisory Board.
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