Abstract
This paper put forth two novel approaches to effectively improve the performance of mispronunciations detection in English learners speech. On one hand, a distance measure called Kullback–Leibler Divergence (KLD) between Hidden Markov Models (HMMs) is introduced to optimize the probability space of a posteriori probability; On the other hand, back end processing of normalization based on the variants of speakers is introduced to improve the performance of the system. Experiments on a database of 6360 syllables pronounced by 50 speakers with varied pronunciation proficiency indicate the promising effects of these methods by decreasing the FRR from 58 to 44 % at 20 % FAR.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Franco H, Neumeyer L, Kim Y, Ronen O (1997) Automatic pronunciation scoring for language instruction. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP), vol 2, pp 1471–1474
Neumeyer L, Franco H, Weintraub M, Price P (1996) Automatic text-independent pronunci-ation scoring of foreign language student speech. In: Proceedings of the international conference on spoken language processing, vol 3, pp 1457–1460
Strik H, Truong K, Wet FD, Cucchiarini C (2009) Comparing different approaches for automatic pronunciation error detection. Speech Commun 51(10):845–852
Truong K (2006) Automatic pronunciation error detection in Dutch as a second language: an acoustic-phonetic approach
Dong B, Zhao QW, Yan YH (2006) Automatic scoring of flat tongue and raised tongue in computer-assisted Mandarin learning. Chin Spok Lang Process 580–591
An LL, Wu YN, Liu Z, Liu RS (2012) An application of mispronunciation detecting network for computer assisted language learning system. J Electron Inf Technol 34(9):2085–2090
Ge Z, Sharma SR, Smith, MJ (2013) Improving mispronunciation detection using adaptive frequency scale. Comput Electr Eng 39(5):1464–1472
Huang C, Zhang F, Soong FK, Chu M (2008) Mispronunciation detection for mandarin Chinese. In: Ninth annual conference of the international speech communication association
Goldberger J, Aronowitz H (2005) A distance measure between GMMs based on the un-scented transform and its application to speaker recognition. In: INTERSPEECH, pp 1985–1988
Acknowledgments
This work is supported by the Foundation of Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology, No. CRKL150105). This work is also supported by the Innovation Project of GUET Graduate Education, No. YJCXS201543.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, G., Qin, C., Shen, Y., Zhou, Y. (2017). Improvement in Text-Dependent Mispronunciation Detection for English Learners. In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-38771-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-38771-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-38769-7
Online ISBN: 978-3-319-38771-0
eBook Packages: EngineeringEngineering (R0)