01
Unequal data availability
Languages differ greatly in the volume, quality and legal usability of text, speech and aligned multilingual data.
European digital language equality
European Language Equality: building an AI future that works in every European language
Pangeanic contributed industry expertise, machine translation analysis and speech data generation to the European Language Equality programme, helping define the data, technology and policy foundations required for digital language equality by 2030.
2021 to 2023 ELE and its follow-up action ELE2
70+ languages European languages examined by the programme
50+ partners Research, industry and language communities
2030 roadmap Strategic horizon for digital language equality
Pangeanic contributed Industry analysis and speech data generation
The European language divide
AI capability remains unevenly distributed across Europe’s languages
English and a small group of commercially important languages benefit from large datasets, extensive research investment and mature language technology ecosystems.
Many national, regional, co-official and endangered languages have fewer digital texts, less transcribed speech, limited evaluation data and a smaller range of deployable models.
This imbalance affects access to public services, education, media, cultural participation and emerging AI systems. A language without sufficient digital resources risks becoming less useful in the environments where citizens increasingly communicate, work and obtain information.
02
Uneven model support
Languages with smaller markets often have fewer production-ready tools, models and domain-specific systems.
03
Limited evaluation
Reliable benchmarks may be unavailable for comparing model quality, safety and linguistic coverage.
04
Digital exclusion
Citizens may be required to change language before they can use digital services or interact effectively with AI.
The European Language Equality mission
Turn language equality into a measurable European technology programme
ELE combined evidence gathering, technology assessment, community consultation and strategic planning to define how Europe could achieve digital language equality.
01
Map language support
Assess the availability of datasets, technologies, services, research capacity and deployable tools for European languages.
02
Identify capability gaps
Determine where languages lack speech resources, parallel data, evaluation sets, models or production infrastructure.
03
Consult the ecosystem
Bring together researchers, companies, public institutions, professional communities and language representatives.
04
Define priorities
Establish research, funding, data and deployment priorities according to language needs and expected social impact.
05
Build a roadmap
Translate analysis into a strategic research, innovation and implementation agenda extending towards 2030.
06
Move into implementation
Test practical methods for generating missing data and improving support for languages with fewer digital resources.
Foundations of digital language equality
Equality depends on a complete chain from data collection to operational deployment
A language cannot become digitally equal through a single dataset or model. It requires sustained infrastructure across the entire AI lifecycle.
DATA
Representative language data
Text, speech, terminology, aligned corpora and instruction data must reflect real users, domains and language varieties.
RIGHTS
Legally usable resources
Provenance, permissions, privacy and intellectual property determine whether data can support sustainable AI development.
MODEL
Language and domain models
Models must be adapted to the linguistic structures, terminology and operational contexts of each language community.
ALIGN
Human and cultural alignment
Native speakers and domain specialists are needed to evaluate fluency, relevance, safety and cultural appropriateness.
TEST
Comparable evaluation
Gold-standard test sets make it possible to measure progress and expose unequal model performance across languages.
DEPLOY
Deployable infrastructure
Public institutions and enterprises need secure APIs, private models and operational systems that can use these resources.
Pangeanic in European Language Equality
From strategic analysis to practical generation of missing language data
Pangeanic contributed through two connected lines of work: analysis of machine translation and its societal impact in ELE, followed by speech corpus generation and data augmentation work in ELE2.
INDUSTRY
Industry perspective
Pangeanic contributed practical experience from the development and deployment of multilingual technologies in commercial and public sector environments.
MT
Machine translation analysis
The company contributed to the deep-dive work examining how machine translation affects digital language equality and society.
IMPACT
Social and operational impact
The analysis considered how translation technology changes access to information, public services and multilingual participation.
SPEECH
Speech corpus generation
Through ELE2, Pangeanic investigated scalable methods for creating additional speech data for the languages of Spain.
AUGMENT
Audio data augmentation
The work explored how controlled transformations of existing recordings could expand datasets while preserving useful linguistic information.
LOW RESOURCE
Support for smaller datasets
The project addressed a recurring challenge in multilingual AI: how to develop useful systems when naturally available training data is limited.
Machine translation deep dive
Translation technology can expand participation, but coverage alone does not create equality
Machine translation gives citizens access to information beyond the language in which it was originally published. It can support public administration, commerce, education and communication across borders.
Its benefits remain uneven when model quality varies sharply between languages or when specialised terminology, regional varieties and local institutions are poorly represented in training data.
Digital language equality therefore requires more than adding a language label to a general system. It requires representative datasets, transparent evaluation, native-language expertise and models that can perform reliably in real operational contexts.
ACCESS
Wider access to information
Translation systems can make institutional, educational and commercial content available across language communities.
QUALITY
Unequal performance
The same system may deliver useful results for one language and unreliable output for another.
DOMAIN
Domain-specific requirements
Legal, medical, administrative and technical language requires specialised data and evaluation.
HUMAN
Human validation
Native speakers and subject specialists remain essential for defining acceptable quality and detecting failure.
ELE2 speech data work
Generating more useful speech data when natural resources are limited
Pangeanic’s ELE2 work examined the generation of an expanded speech corpus for the languages of Spain through controlled audio augmentation.
01
Collect source recordings
Begin with existing speech samples and associated linguistic information from the target languages.
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02
Apply controlled transformations
Generate variations using methods that modify selected acoustic properties without changing the linguistic content.
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03
Validate data quality
Review whether the resulting audio remains intelligible, linguistically useful and suitable for model development.
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04
Expand the corpus
Produce a larger and more varied dataset for experiments involving lower-resource speech technology.
Linguistic diversity in Spain
Multilingual AI must account for languages with different data volumes, markets and institutional contexts
Spain provides a concentrated example of the European language technology challenge. Several official and co-official languages coexist with regional varieties and languages whose digital resources differ substantially in scale and maturity.
Building useful speech and language systems requires language-specific collection, native-speaker validation and careful treatment of pronunciation, terminology, dialectal variation and code-switching.
Programme outcomes
Evidence, strategy and implementation pathways for European languages
ELE established a common framework for understanding digital language inequality and coordinating long-term European action.
2030
European roadmap
A strategic horizon for achieving much stronger and more balanced technological support across European languages.
70+
Languages considered
The programme extended analysis beyond the largest official languages to reflect Europe’s wider linguistic diversity.
SRIIA
Strategic agenda
Research, innovation and implementation priorities were consolidated into a shared European framework.
Data
Practical resource generation
ELE2 extended the strategic work through focused experiments and initiatives addressing missing language resources.
European Language Equality in 2026
The language question is becoming central to European AI sovereignty
General-purpose models can support many languages, but nominal coverage does not guarantee comparable performance, local control or suitability for high-value institutional tasks.
Europe increasingly requires smaller and specialised models adapted to particular languages, domains and security environments. These systems depend on precisely the resources highlighted by ELE: governed data, representative evaluation and deployable language infrastructure.
Digital language equality has therefore become part of a larger strategic issue. Europe’s capacity to operate AI in its own languages affects public administration, industrial competitiveness, cultural continuity and technological sovereignty.
SLM
Specialised language models
Smaller models can be adapted to specific languages and tasks when appropriate data is available.
SOVEREIGN
Sovereign deployment
Institutions can retain greater control over infrastructure, data movement and model behaviour.
EVAL
Language-specific evaluation
Comparable benchmarks reveal where general systems underperform for particular communities.
PUBLIC
Multilingual public services
Citizens should be able to interact with digital institutions without abandoning their preferred language.
From language policy to operational AI
What digital language equality requires from organisations
Public institutions and enterprises need practical data and model programmes that turn linguistic inclusion into measurable capability.
01
For public administrations
Build multilingual services that provide comparable access and quality across official and co-official languages.
02
For AI developers
Source representative data and native-speaker feedback for languages insufficiently covered by general models.
03
For regulated enterprises
Evaluate multilingual systems before deploying them in legal, financial, healthcare or customer-facing workflows.
04
For sovereign AI programmes
Develop language-specific models, datasets and infrastructure under European governance and operational control.
Public evidence
Programme documentation, publications and Pangeanic technical work
These resources document the European Language Equality objectives, strategic agenda and Pangeanic’s contributions to machine translation analysis and speech data generation.
Official programme
European Language Equality
Official programme website covering the consortium, strategic agenda, roadmap and European language assessments.
Explore the programme →Pangeanic contribution
Machine translation and digital language equality
Pangeanic’s account of its industry role and contribution to the machine translation deep dive.
Read the project article →ELE2 technical report
Generation of a large speech corpus for the languages of Spain
Technical report documenting Pangeanic’s work with audio data augmentation and speech corpus generation.
Read the technical report →Research publication
Overview of the ELE project
Academic overview of the programme’s objectives, methodology and strategic role in European language technology.
Read the publication →Pangeanic research
Research and publications
Explore Pangeanic publications covering multilingual AI, machine translation, privacy and language data.
View research →European project portfolio
European AI and language technology projects
Place European Language Equality within Pangeanic’s broader work in AI data, multilingual infrastructure and sovereign AI.
Explore all projects →Continue exploring
From European language strategy to multilingual data, models and cultural access
European Language Equality connects with Pangeanic projects that turned language policy into reusable datasets, direct translation models and deployable multilingual technology.
European project portfolio
European AI and Language Technology Projects
Explore Pangeanic’s European work across AI data, multilingual models, privacy, accessibility and sovereign infrastructure.
View all projects → Direct multilingual modelsNTEU
Discover the 506 direct neural translation directions created between the official EU languages other than English.
Explore NTEU → Reusable bilingual dataNEC TM Data
Explore the European infrastructure developed to recover, search and reuse translation memories as language data.
Explore NEC TM Data → Cultural and linguistic accessAI4Culture
Discover multilingual AI tools, datasets and validation workflows for European cultural heritage collections.
Explore AI4Culture →Multilingual and sovereign AI
Build AI that works in the languages your users actually speak
Pangeanic provides ethically sourced multilingual datasets, speech data, native-speaker evaluation, model alignment and sovereign language infrastructure for enterprises and public institutions.