Resource: Global TIMIT Mandarin Chinese-Guanzhong Dialect

Reference Global TIMIT Mandarin Chinese-Guanzhong Dialect
Date of Submission Dec. 16, 2020, 8:16 p.m.
Status accepted
ISLRN 748-031-550-067-2
Resource Type Primary Text
Media Type Text, Audio
Language Mandarin Chinese
Format/MIME Type text/plain, audio/x-flac
Size 680955 KB
Access Medium Web Download


Global TIMIT Mandarin Chinese-Guanzhong Dialect was developed by the Linguistic Data Consortium and Xi'an Jiaotong University and consists of approximately five hours of read speech and transcripts in the Guanzhong dialect of Mandarin Chinese as spoken in Shannxi province.

The Global TIMIT project aimed to create a series of corpora in a variety of languages with a similar set of key features as in the original TIMIT Acoustic-Phonetic Continuous Speech Corpus (LDC93S1) which was designed for acoustic-phonetic studies and for the development and evaluation of automatic speech recognition systems. Specifically, these features included:

* A large number of fluently-read sentences, containing a representative sample of phonetic, lexical, syntactic, semantic, and pragmatic patterns
* A relatively large number of speakers
* Time-aligned lexical and phonetic transcription of all utterances
* Some sentences read by all speakers, others read by a few speakers, and others read by just one speaker


Global TIMIT Mandarin Chinese-Guanzhong Dialect consists of 50 speakers reading 120 sentences selected from Chinese Gigaword Fifth Edition (LDC2011T13). Among the 120 sentences, 20 sentences were read by all speakers, 40 sentences were read by 10 speakers, and 60 sentences were read by one speaker, for a total of 3220 sentence types.

The corpus was recorded at Xi'an Jiaotong University, Xi'an, China. Speakers (25 female, 25 male) were born in Weinan, Shannxi and spoke the Guanzhong dialect.

All speech data are presented as 16kHz, 16-bit flac compressed wav files. Each file has accompanying phone, word, and tone segmentation files, as well as Praat TextGrid files.


The authors acknowledge the generous support of the University of Pennsylvania's Office of the Vice Provost for Global Initiatives (PennGlobal) through its Penn China Research and Engagement Fund, and the School of Arts and Sciences through its Global Engagement Fund as well as the Linguistic Data Consortium.

Version 1.0
Creator Mark Liberman , Jiahong Yuan , Yue Jiang , Juhong Zhan , Hongjian Han , Zuohao Xu , Haiyan Zhou
Distributor Linguistic Data Consortium
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