Introducing MELI: The Mandarin-English Language Interview Corpus

Suyuan Liu, Molly Babel


Abstract
We introduce the Mandarin–English Language Interview (MELI) Corpus, an open-source resource of 29.8 hours of speech from 51 Mandarin–English bilingual speakers. MELI combines matched sessions in Mandarin and English with two speaking styles: read sentences and spontaneous interviews about language varieties, standardness, and learning experiences. Audio was recorded at 44.1 kHz (16-bit, stereo). Interviews were fully transcribed, force-aligned at word and phone levels, and anonymized. Descriptively, the Mandarin component totals ~14.7 hours (mean duration 17.3 minutes) and the English component ~15.1 hours (mean duration 17.8 minutes). We report token/type statistics for each language and document code-switching patterns (frequent in Mandarin sessions; more limited in English sessions). The corpus design supports within-/cross-speaker, within/cross-language acoustic comparison and links speech content to speakers’ stated language attitudes, enabling both quantitative and qualitative analyses. The MELI Corpus will be released with transcriptions, alignments, metadata, scans of labelled maps and documentation under a CC BY-NC 4.0 license.
Anthology ID:
2026.lrec-1.468
Volume:
Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:
May
Year:
2026
Address:
Palma de Mallorca, Spain
Editors:
Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:
LREC
SIG:
Publisher:
ELRA Language Resource Association
Note:
Pages:
5896–5904
Language:
External URL:
https://lrec.elra.info/lrec2026-main-468
DOI:
10.63317/3umiyc4sxwhk
Bibkey:
Cite (ACL):
Suyuan Liu and Molly Babel. 2026. Introducing MELI: The Mandarin-English Language Interview Corpus. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5896–5904, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):
Introducing MELI: The Mandarin-English Language Interview Corpus (Liu & Babel, LREC 2026)
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