MekongPhon: A Large-Scale Parallel IPA Corpus for Lao and Khmer

Abstract

High-quality International Phonetic Alphabet (IPA) transcriptions are a foundational resource for speech and language technologies, yet existing tools for many low-resource languages remain limited in accuracy and scope. In this work, we present MekongPhon, a large-scale, high-quality parallel IPA corpus for Lao and Khmer. The corpus contains 1.3 million Khmer and 367 thousand Lao orthographic–IPA pairs, meticulously aligned and verified. When used to train Transformer-based sequence-to-sequence models, MekongPhon enables exceptionally accurate IPA generation, achieving under 2% Character Error Rate (CER) on held-out test sets. We further introduce linguistically informed Lao and Khmer transliteration tools that offer high-speed IPA conversion, outperforming Epitran by 6-71 CER points despite trading some accuracy for efficiency. All data, code, and pretrained models are publicly released to support future research and development in low-resource language technologies.

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