Resource: Global TIMIT Mandarin Chinese
|Reference||Global TIMIT Mandarin Chinese|
|Date of Submission||March 18, 2021, 7:09 p.m.|
|Resource Type||Primary Text|
|Media Type||Text, Audio|
|Format/MIME Type||text/plain, audio/x-flac|
|Access Medium||Web Download|
Global TIMIT Mandarin Chinese was developed by the Linguistic Data Consortium and Shanghai Jiao Tong University and consists of approximately five hours of read speech and transcripts in Mandarin Chinese.
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
Global TIMIT Mandarin Chinese 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 Shanghai Jiao Tong University, China. Speakers (25 female, 25 male) were students at the university and all achieved Class 2 Level 1 or better on Putonghua Shuiping Ceshi (the national standard Mandarin proficiency test).
All speech data are presented as 16kHz, 16-bit flac compressed wav files. Each file has accompanying phone and word 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.
|Creator||Mark Liberman , Jiahong Yuan , Hongwei Ding , Sishi Liao , Yuqing Zhan|
|Distributor||Linguistic Data Consortium|
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