Dialect classification based on the speed and the pause of speech utterances*
Author:
Funder
National Research Foundation of Korea
Ministry of Science and ICT, South Korea
Publisher
The Korean Society of Speech Sciences
Subject
Materials Science (miscellaneous)
Link
http://www.eksss.org/download/download_pdf?doi=10.13064/KSSS.2023.15.2.043
Reference37 articles.
1. Bhattacharjee, U., & Sarmah, K. (2013, March). Language identification system using MFCC and prosodic features. Proceedings of the 2013 International Conference on Intelligent Systems and Signal Processing (ISSP) (pp. 194-197). Vallabh Vidyanagar, India. 10.1109/ISSP.2013.6526901
2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. 10.1023/A:1010933404324
3. Chittaragi, N. B., & Koolagudi, S. G. (2019). Acoustic-phonetic feature based kannada dialect identification from vowel sounds. International Journal of Speech Technology, 22(4), 1099-1113. 10.1007/s10772-019-09646-1
4. Chowdhury, S. A., Ali, A., Shon, S., & Glass, J. (2020, October). What does an end-to-end dialect identification model learn about non-dialectal information? Proceedings of the INTERSPEECH 2020 (pp. 462-466). Shanghai, China. 10.21437/Interspeech.2020-2235
5. Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357-366. 10.1109/TASSP.1980.1163420
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