What Are Low-Resource Languages?

Low-resource languages are languages for which limited linguistic data and computational tools exist, particularly in digital or technological formats. These languages typically lack the kinds of structured, large-scale datasets required to train AI models or support automated systems like speech recognition and machine translation.

Globally, there are over 7,000 spoken languages, yet over 90% of online content is produced in fewer than two dozen of them. Languages like English, Spanish, French, and Mandarin benefit from rich datasets, advanced computational tools, and ongoing research. In contrast, languages like Luganda, Guarani, Inuktitut, or Sesotho have relatively little presence in digital platforms, even though they serve millions of speakers in everyday life.

Low-resource status can apply across multiple dimensions:

• Lack of digitised texts or corpora for training language models.
• No standard orthography or inconsistent writing systems.
• Absence of audio data, such as transcribed conversations or interviews.
• Limited linguistic tools, such as parsers, speech synthesisers, or spellcheckers.
• Minimal institutional support for research or language development.

This underrepresentation stems from colonial histories, economic disparities, and the prioritisation of dominant national or global languages in digital infrastructure. Often, speakers of low-resource languages are marginalised communities with limited influence over technological priorities.

Understanding which languages are low-resource and why is essential. It not only sets the stage for technical solutions but ensures that speech collection efforts respond to broader social and cultural dynamics. For example, while Afrikaans and Zulu are official languages in South Africa, Zulu remains underrepresented in AI due to fewer available digital corpora and less commercial investment.

Language status is fluid—it depends not only on speaker numbers but also on institutional backing, digital development, and linguistic standardisation. What matters most is how we respond to this gap with deliberate, inclusive action.

Challenges in Data Collection

Collecting speech data in low-resource languages is fraught with complexity. Unlike high-resource languages where recordings, transcripts, and corpora are readily available online, low-resource languages often require building everything from the ground up—sometimes starting with the writing system itself.

The main challenges include:

• Lack of Orthographic Standards: Many low-resource languages are oral in nature. Without a consistent writing system, it becomes difficult to transcribe speech, align it with audio, or build reliable language models. Even when orthographies exist, they may vary across regions or communities.
• Geographical and Infrastructural Barriers: Speakers of minority languages often reside in remote or rural areas with limited access to electricity, internet, or mobile devices. This makes field recording, digital consent collection, and cloud-based storage difficult or impossible.
• Small and Fragmented Speaker Base: Some endangered languages have fewer than 1,000 fluent speakers, making it difficult to collect representative samples across different dialects, speaking styles, and demographics. Even larger communities may be spread across political borders, complicating logistics and permissions.
• Cultural and Political Sensitivities: Language is tightly bound to identity. In some communities, past marginalisation or exploitation has created deep mistrust toward external researchers or tech companies. In others, the language may be viewed as sacred or private, not for public dissemination.
• Low Incentives for Participation: In the absence of clear and immediate benefits, speakers may see little value in contributing their voice to a distant or abstract cause. If the output of the project is not shared with the community or if the process is opaque, participation may dwindle.
• Resource Limitations for Annotators: Transcription and annotation often require bilingual individuals with technical training. However, many communities lack a pool of trained linguists or transcriptionists who are familiar with annotation tools or phonetic conventions.

Despite these challenges, a growing ecosystem of researchers, language activists, and community-led initiatives is forging new paths to overcome these barriers. With culturally sensitive methods, thoughtful planning, and the right tools, meaningful progress is possible.

Community-Driven Collection Strategies

One of the most effective approaches to speech data collection in low-resource languages is community-driven collaboration. Rather than viewing speakers as passive subjects, ethical data collection treats them as co-creators—owners of their language and its representation in AI systems.

Key elements of a community-centred approach include:

• Local Partnerships and Co-Design: Partnering with community organisations, schools, cultural institutes, or radio stations creates access and legitimacy. These partnerships often provide the infrastructure and outreach capabilities that external researchers lack.
• Respect for Cultural Norms: Before recording begins, it’s critical to understand community customs. For instance, in some cultures, it may be inappropriate to record women’s voices publicly, or certain words or topics may be taboo. A respectful approach involves local consultation and adaptation of the methodology.
• Informed Consent and Data Sovereignty: Participants must be made fully aware of how their voice data will be used, stored, and potentially shared. Consent processes should be clear and available in the local language. Where possible, communities should have ongoing access to the data and a say in its future use.
• Training and Capacity Building: Training local speakers as data collectors, transcribers, or validators not only improves data quality but empowers the community. These skills can extend beyond the project into education, media, and local enterprise.
• Equitable Compensation: Contributors should be compensated fairly for their time and expertise. This can be monetary or include community incentives such as mobile data, solar equipment, school support, or shared technology infrastructure.
• Feedback and Visibility: Communities should receive tangible outcomes—whether in the form of digital tools built using the data, recordings made available in archives, or workshops where findings are shared. Visibility creates a cycle of trust and engagement.

An example of this approach can be seen in initiatives where schoolchildren, elders, and teachers collectively contribute to voice datasets, sharing stories, proverbs, and daily conversation. Such efforts preserve oral traditions while supplying speech data with authentic linguistic variety.

The long-term impact of community-driven models is more sustainable, ethical, and inclusive. It reinforces the dignity of underrepresented speakers and aligns speech collection with language preservation goals.

Tools and Techniques for Scalable Collection

Modern tools have made it increasingly feasible to collect, process, and annotate speech data even in resource-constrained environments. The key lies in choosing technologies that are adaptable, offline-capable, and user-friendly.

Some of the most effective tools and techniques include:

• Mobile Recording Apps: Tools like Aikuma, Lingua Libre, and custom apps allow users to record speech using their mobile phones. These apps are often designed with multilingual prompts and simple interfaces, enabling broad participation.
• Offline and Low-Power Devices: In areas without consistent power or internet, lightweight digital audio recorders (such as Zoom H1n or Sony IC recorders) provide reliable recording solutions. Solar chargers and offline syncing tools enhance portability.
• Open-Source Annotation Platforms: Tools like ELAN, PRAAT, and SayMore enable users to annotate and transcribe audio. These tools support tiered annotations, time alignment, and metadata tagging. They are particularly useful in linguistic fieldwork.
• Cloud-Based Collaborative Platforms: Where connectivity allows, platforms such as Mozilla Common Voice, Coqui Studio, or LIG-Aikuma allow distributed contribution and community validation. Volunteers can listen to, verify, or label recordings from anywhere.
• Custom Scripts and Templates: Simple spreadsheets or form templates can be used to track speaker metadata (age, gender, dialect), consent forms, and recording quality. Using structured formats ensures consistent and usable outputs.
• AI-Assisted Preprocessing: Automatic speech segmentation tools can help cut long recordings into utterances, while pre-trained models can suggest draft transcriptions in dominant languages for bilingual teams to correct. This hybrid approach accelerates workflows.
• Data Storage and Version Control: Using version control systems or secure cloud drives allows teams to back up recordings, transcripts, and metadata. Platforms like GitHub or Airtable (with local backup options) offer lightweight solutions.

The success of any tool depends on the workflow. Careful planning, local testing, and basic technical training can turn simple tools into powerful enablers for multilingual voice data collection at scale.

Impact and Use Cases in AI

The value of collecting speech data in low-resource languages goes beyond the dataset itself. It unlocks access, dignity, and cultural continuity—especially when incorporated into real-world technologies.

Common and emerging use cases include:

• Voice Assistants for Local Services: When speech datasets are used to train AI models, it becomes possible to build voice-enabled interfaces in local languages. These can support agricultural advice, maternal health information, or banking services in rural areas.
• Inclusive Education Platforms: E-learning systems can use voice data to deliver lessons in native tongues. This is especially valuable for early childhood development and adult literacy where first-language instruction is key to comprehension.
• Emergency and Health Communications: Real-time translation and speech systems trained on local data can support multilingual emergency announcements, healthcare messaging, and vaccination campaigns.
• Preservation of Oral Heritage: Traditional knowledge, songs, stories, and oral histories can be documented and archived. This not only preserves the cultural record but offers material for revitalisation efforts and curriculum development.
• Multilingual ASR and MT Systems: Speech data enables automatic speech recognition and machine translation tools to handle code-switching and dialectal variation common in multilingual societies. This increases accessibility for public services, legal systems, and news media.
• Voice Biometric Identity Systems: In regions where literacy is low or legal identity is lacking, voiceprint-based systems trained on local language samples can support secure authentication for social services or microfinance.

These use cases demonstrate how speech data fuels tangible advances in inclusion. When collected ethically and used thoughtfully, it helps close the digital divide and ensures that technology speaks in every voice.