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New dataset ELARSpeech boosts AI for 675 underrepresented languages

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ELARSpeech, a new dataset containing over 30,000 hours of speech across 675 underrepresented languages. This resource, curated from the Endangered Languages Archive, aims to address the underrepresentation of linguistic long-tail languages in existing multilingual speech technologies. Experiments using ELARSpeech demonstrate its utility in extending Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) capabilities to these diverse languages, with potential benefits from pseudo-labeling unlabeled data. AI

IMPACT Enables development of speech technologies for a wider range of languages, potentially improving accessibility and inclusivity.

RANK_REASON The cluster contains a research paper detailing a new dataset for underrepresented languages. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset ELARSpeech boosts AI for 675 underrepresented languages

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The cluster contains a research paper detailing a new dataset for underrepresented languages. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sangmin Lee, Yejin Jeon, David Ifeoluwa Adelani ·

    ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies

    arXiv:2610.19388v1 Announce Type: new Abstract: Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresent much of the linguistic long tail. To address this gap, we introduce ELARSpeech,…

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