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Code-Switched and Dialectal Speech Data

Speech training data for code-switching, dialect variation, and low-resource language AI , with native speaker annotators across 500+ locales and 80+ languages.

Global AI products fail their most linguistically diverse users first. Models trained on standard-variety, high-resource language data consistently underperform on regional dialects, code-switched speech, and minority language varieties. Appen's multilingual AI data service closes this gap with authentic speech collection and annotation across 500 global locales, including the code-switching patterns and dialectal variation that mainstream datasets systematically underrepresent.

What Appen Delivers

Code-Switching Data Collection

Authentic recordings of natural code-switching behaviour between language pairs including English-Spanish, Hindi-English, Arabic-French, and Mandarin-Cantonese. Code-switched speech is collected in naturalistic conversational settings, capturing the genuine mixing patterns that occur in multilingual communities rather than artificially constructed examples.

Dialectal Variation Coverage

Phonologically and lexically annotated recordings across regional dialect continua for major languages, ensuring models learn to recognise and produce the varieties spoken by regional populations rather than defaulting to a single prestige standard. Coverage includes 500+ locales across 100 languages.

Low-Resource Language Data

Specialised collection programmes for low-resource and endangered language varieties where commercial training data is unavailable or insufficient. Appen works with community linguists and native speaker networks to produce ethically collected, high-quality data for languages that commercial AI has historically neglected.

Multilingual Transcription and Translation

Verbatim and normalised transcription with language identification at the token level for code-switched recordings, plus culturally nuanced translation annotation that captures meaning transfer beyond literal translation for multilingual NLP model training.

Why Linguistic Diversity Is a Data Problem

Speech recognition accuracy gaps between standard and dialectal varieties are not model architecture problems. They are data problems. Models trained on diverse, high-quality dialectal and code-switched data close these gaps. Models trained on standard varieties alone do not, regardless of model scale.

Appen has delivered multilingual speech data for teams including Microsoft Translate's equitable knowledge access programme and multilingual LLM fine-tuning projects. Our global contributor network is the infrastructure that makes genuine linguistic diversity achievable at production scale.

Related Resources

Blog

Multilingual NLP: Code-Switching, Variants, & Dialectal Expansion

EMNLP 2025 spotlights dialects, variants, and code-switching. Why closing the dialect gap matters and how Appen builds inclusive data and evaluation.

Read article
Case Study

How Microsoft is Advancing Equitable Knowledge Access with AI Translation

The partnership between Microsoft Translator and Appen underscores the critical role that high-quality data plays in developing AI technologies. With Appen’s support, Microsoft was able to expand its language portfolio to 110 languages.

Read article
Research

Multilingual LLM Translation: Evaluating Cultural Nuance in Generative AI

Discover where multilingual AI translation falls short and why human oversight is key to accurate localisation.

Read article

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