Stress Detection of English Words for a CAPT System Using Word-Length Dependent GMM-Based Bayesian Classifiers

Author:

CHEN Liang-Yu1,JANG Jyh-Shing Roger2

Affiliation:

1. Institute of Information Systems and Applications, National Tsing Hua University

2. Department of Computer Science, National Tsing Hua University

Publisher

Graduate School of Information Sciences, Tohoku University

Subject

General Medicine

Reference16 articles.

1. 1) Charniak, E., Blaheta, D., Ge, N., Hall, K., Hale, J., and Johnson, M., ``BLLIP 1987-1989 WSJ Corpus Release 1,'' Linguistic Data Consortium, Philadelphia (2000). Available: http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2000T43

2. 2) Chen, J. C., and Jang, J. S. R., ``TRUES: Tone recognition using extended segments,'' ACM Trans. Asian Language Information Processing (TALIP), 7: 1-23 (2008).

3. 3) Chen, L. Y., and Jang, J. S. R., ``Automatic pronunciation scoring using learning to rank and DP-based score segmentation,'' in Proc. INTERSPEECH 2010, Makuhari, Japan, 761-764 (2010).

4. 4) Cincared, T., Gruhn, R., Hacker, C., Nöth, E., and Nakamura, S., ``Automatic pronunciation scoring of words and sentences independent from the non-native's first language,'' Computer Speech and Language, 23: 65-88 (2009).

5. 5) Kochanski, G., Grabe, E., Coleman, J., and Rosner, B., ``Loudness predicts prominence: fundamental frequency lends little,'' J. Acoustic Society of America, 118: 1038-1054 (2005).

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