Title

The Electromagnetic Articulography Mandarin Accented English (EMA-MAE) Corpus of Acoustic and 3D Articulatory Kinematic Data

Document Type

Conference Proceeding

Language

eng

Format of Original

5 p.

Publication Date

5-4-2014

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Original Item ID

doi: 10.1109/ICASSP.2014.6855102

Abstract

There is a significant need for more comprehensive electromagnetic articulography (EMA) datasets that can provide matched acoustics and articulatory kinematic data with good spatial and temporal resolution. The Marquette University Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus provides kinematic and acoustic data from 40 gender and dialect balanced speakers representing 20 Midwestern standard American English L1 speakers and 20 Mandarin Accented English (MAE) L2 speakers, half Beijing region dialect and half are Shanghai region dialect. Three dimensional EMA data were collected at a 400 Hz sampling rate using the NDI Wave system, with articulatory sensors on the midsagittal lips, lower incisors, tongue blade and dorsum, plus lateral lip corner and tongue body. Sensors provide three-dimensional position data as well as two-dimensional orientation data representing the orientation of the sensor plane. Data have been corrected for head movement relative to a fixed reference sensor and also adjusted using a biteplate calibration system to place the data in an articulatory working space relative to each subject's individual midsagittal and maxillary occlusal planes. Speech materials include isolated words chosen to focus on specific contrasts between the English and Mandarin languages, as well as sentences and paragraphs for continuous speech, totaling approximately 45 minutes of data per subject. A beta version of the EMA-MAE corpus is now available, and the full corpus is in preparation for public release to help advance research in areas such as pronunciation modeling, acoustic-articulatory inversion, L1-L2 comparisons, pronunciation error detection, and accent modification training.

Comments

Published as part of the proceedings of the conference, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014: 7719-7723. DOI.