Underrepresented Languages in AI: Consequences for Speech Technology

What Languages Are Most Underrepresented in Speech Corpora?

Closing the Gap on Missing Language Datasets

Language inclusion is now a core requirement for responsible AI, not a side issue. Yet speech technology still reflects a sharp imbalance: a handful of languages have rich datasets, while many others, including widely spoken regional languages, remain poorly represented.

This matters across real products. Voice assistants, learning tools, public service platforms, and accessibility systems all rely on robust speech corpora, including high-quality annotations such as timestamp alignments. When language coverage is thin, communities are left with lower accuracy, fewer features, and reduced digital access.

This article outlines where underrepresentation is most severe, why it persists, and what practical steps can close the gap.

Defining Underrepresentation in Speech AI

In speech AI, “underrepresented” describes languages that are missing from training corpora or appear only in limited, low-quality, or non-representative datasets. It is not defined by speaker count alone.

A useful assessment separates:

• Number of native speakers vs. digital resource availability: Large communities can still have almost no usable AI-ready speech data.
• Presence in research vs. presence in production models: A language may appear in papers but remain absent from real products such as ASR tools or voice assistants.
• Standardised languages vs. regional variants: Even well-resourced languages often exclude important dialects.

Common drivers of underrepresentation include:

• Limited written standardisation: Some languages have few agreed orthographies, making transcription and annotation harder.
• Uneven funding priorities: Investment often favours commercially dominant languages.
• Trust and ownership concerns: Communities may resist participation if data use is unclear or extractive.

A practical working definition includes languages that:

• lack publicly available or well-annotated speech corpora;
• have little or no support in commercial speech products;
• are consistently underprioritised in research despite active use by speech communities.

These languages represent the largest blind spots in current speech AI, and those blind spots tend to affect already marginalised communities most.

Global Overview of Missing Languages

The world is home to over 7,000 languages, yet only a few hundred are represented in usable speech corpora, and even fewer are included in commercial speech products. A closer look at the global map of underrepresentation reveals striking disparities.

Africa: Endangered Click Languages

The Khoisan languages of southern Africa, known for their unique click consonants, are among the most underrepresented. Languages like !Xóõ, N|uu, and ǂʼAmkoe are spoken by only a few hundred individuals, with extremely limited digital presence.

Despite being phonetically rich and linguistically significant, their data is scarce due to:

• Remote and dispersed speaker populations;
• A history of marginalisation and language suppression;
• Technical challenges in transcribing complex phonemes.

Asia: The Disappearance of Ainu

Ainu, once widely spoken in Hokkaido, Japan, is now critically endangered. While revitalisation efforts are underway, there is almost no speech data available for AI training. Similar patterns are seen in minority Tibeto-Burman languages and languages of Arunachal Pradesh, India, which are spoken in geographically isolated regions.

The Americas: Mixtec and Mayan Variants

Mexico’s Mixtec language is not one language but a family of closely related dialects spoken by over 500,000 people. Many of these variants are mutually unintelligible, but speech data efforts have lumped them together or overlooked them entirely.

Mayan languages like K’iche’, Q’eqchi’, and Yucatec Maya have large speaker bases but lack corpus diversity in terms of age, gender, and dialectal variation.

Europe: Saami and Romani Gaps

Despite Europe’s robust digital infrastructure, underrepresentation exists. The Saami languages of northern Scandinavia are often excluded from national language technology policies, and Romani dialects—spoken by over 10 million people worldwide—have barely any speech representation in commercial systems.

The Overlooked Dialects

Even within well-resourced languages like English or Spanish, regional variants are left out. South African English, for example, has unique phonetic and lexical features rarely captured in generic English datasets. Similarly, Caribbean Spanish and African American Vernacular English (AAVE) are often misclassified or mistranscribed.

This global pattern reveals a hierarchy: the more distant a language is from commercial centres of power, the less likely it is to be digitally captured.

Consequences for Speech Technology

The exclusion of underrepresented languages from speech corpora has ripple effects across several dimensions of modern life. These consequences reinforce systemic inequality and obstruct progress in digital inclusion.

1. Inequitable Access to Technology

Speakers of underrepresented languages cannot use voice assistants, transcription tools, or translation apps in their own language or dialect. This creates a digital divide where participation in digital economies and public services is restricted.

For example:

• A Khoisan speaker trying to access a health app may find it only available in English or Afrikaans.
• A Mixtec farmer receiving weather alerts will need to rely on a language they are less comfortable with.

2. Exclusion from Education and Learning Tools

Educational platforms increasingly use speech technology for assessment, pronunciation feedback, and interactive learning. When these tools don’t support a learner’s home language, it hinders literacy and engagement.

Moreover, children who grow up in non-dominant language communities may be forced to code-switch, weakening both linguistic confidence and cultural connection.

3. Barriers to Public Participation

Public services like e-government, transportation, or healthcare are moving toward speech interfaces. When these are not multilingual or dialect-aware, certain populations are effectively silenced in civic processes.

4. Biased AI Systems

AI models trained only on dominant language data are inherently biased. They misrecognise accents, dialects, and minority languages, resulting in:

• Disproportionate error rates;
• Discrimination in hiring (via automated interviews);
• Misinformation through misclassification or mistranslation.

By failing to represent the full linguistic spectrum, we build systems that serve the few while ignoring the many.

Efforts to Address Language Gaps

The good news is that several global and community-based initiatives are actively working to bridge the speech data gap. These efforts are varied in scale and scope, but together they demonstrate a path toward inclusion.

Common Voice by Mozilla

Common Voice is an open-source platform that invites anyone to contribute voice recordings in their language. It currently includes over 100 languages and continues to grow through community partnerships.

Notable achievements:

• Inclusion of languages like Tatar, Kinyarwanda, and Luganda;
• Localised interfaces that allow users to participate in their mother tongue;
• Gender-balanced and dialect-inclusive data collection drives.

ELAR (Endangered Languages Archive)

Based at SOAS University of London, ELAR stores multimedia documentation of endangered languages, including thousands of hours of annotated speech data. While it’s more academic than AI-focused, it’s a valuable resource for foundational data and phonetic diversity.

Masakhane

Masakhane is a grassroots, Africa-centric initiative aimed at natural language processing (NLP) for African languages. While initially text-focused, Masakhane is expanding into speech technology and translation through local partnerships.

Its success is rooted in:

• Open collaboration across countries and disciplines;
• Emphasis on community ownership;
• Sharing tools and frameworks for dataset creation.

Local Data Collectives

Smaller projects are emerging that focus on specific languages or regions. These include:

• University partnerships with Indigenous communities to record oral histories;
• NGOs training youth to collect and annotate local dialect recordings;
• Hackathons to build custom ASR models for school children in marginalised areas.

These efforts reveal that inclusion is not solely the responsibility of tech giants. Communities, researchers, and developers can all play a role in documenting the sounds of the world.

Strategic Recommendations for Inclusion

For those building speech technologies or funding linguistic data efforts, here are key strategies to prioritise underrepresented languages effectively:

1. Focus on Speaker-Centric Value

Instead of collecting data for abstract AI goals, ask: What problems will this solve for the speaker community? Tools for public health, education, or farming advice often yield greater community support and data quality.

2. Work With Local Partners

Community organisations, universities, and cultural leaders must be involved at every stage—from planning to collection to ownership. This ensures consent, relevance, and sustainability.

3. Prioritise Dialectal Diversity

When representing a language, avoid collapsing it into a monolith. Capture:

• Different age groups and genders;
• Rural vs. urban speech;
• Varieties spoken in different provinces or social contexts.

This increases the robustness and fairness of resulting models.

4. Share Data Openly When Ethical

Where privacy and consent allow, publish anonymised datasets under open licences. This fuels more innovation and avoids duplicated effort.

5. Fund Annotation and Metadata

Raw recordings are not enough. Invest in:

• Accurate transcription (especially for oral languages);
• Speaker demographics and context tagging;
• Phonetic-level annotation for linguistic richness.

Without this, even well-collected data remains underutilised.

6. Build for the Edge

Develop speech models that can run on low-power devices and offline settings. This allows real-world deployment in regions with limited connectivity or infrastructure.

7. Train Local Talent

Instead of flying in researchers, train community members to handle data collection, transcription, and model tuning. This empowers long-term maintenance and innovation.

Final Thoughts on Missing Language Datasets

Underrepresented languages in AI are not rare curiosities—they are part of the living, breathing soundscape of our world. By overlooking them, we exclude entire communities from the benefits of digital transformation. But by consciously prioritising their inclusion, we not only build better technology, we build a more just and equitable digital future.

Whether you are a developer, policymaker, or NGO, your involvement can make a real difference. Inclusion in speech AI is not just about technical progress—it’s about human dignity, cultural preservation, and equal opportunity.

Related blog articles

• Tools for Annotating Speech Data: Enhance AI with Precision
• How Do You Collect Speech Data in Low-Resource Languages?
• Promoting Speech Technology in African Languages Through Partnerships

Resources and Links

Wikipedia: Endangered Languages – A foundational reference to understand the scope, causes, and efforts around language endangerment.

Way With Words – Speech Collection – Way With Words offers expert-led speech collection services tailored to complex linguistic and technical environments. Whether you’re building speech models for underrepresented languages or seeking high-quality annotated data, their solutions are designed to bridge the data gap with accuracy, efficiency, and cultural sensitivity.