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基于JSM和MLP改进发音错误检测的方法

DOI: 10.3724/SP.J.1004.2014.02815, PP. 2815-2823

Keywords: 发音错误检测,联合序列多阶模型,多层神经感知,计算机辅助语言学习

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Abstract:

?针对发音错误检测的发音字典生成提出基于联合序列多阶模型(Joint-sequencemulti-gram,JSM)和多层神经感知(Multi-layerperception,MLP)的方法.首先使用JSM模型对发音错误进行建模,将标准发音和错误发音组合为发音对,表示它们之间的对应关系,再使用N元文法来统计各发音对之间的关系,描述错误发音对上下文关系的依赖.最后使用MLP对发音对之间的关系进行重新建模,以学习到在相似的上下文条件下发生的相似的错误.实验证明使用MLP对高阶模型进行概率重估能有效的平滑概率空间,提高了发音错误检测的性能.

References

[1]  Ito A, Lim Y L, Suzuki M. Pronunciation error detection method based on error rule clustering using a decision tree. In: Proceeding of the 6th Annual Conference of the International Speech Communication Association. Tohoku University, Japan: ISCA, 2005. 173-176
[2]  Strika H, Truongb K, Wet F D, Cucchiarini C. Comparing different approaches for automatic pronunciation error detection. Speech Communication, 2009, 51(10): 845-852
[3]  Zhang F, Huang C, Soong F K, Chu M, Wang R H. Automatic mispronunciation detection for Mandarin. In: Proceeding of 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas, Nevada, USA: IEEE, 2008. 5077-5080
[4]  Luo D, Yang X S, Wang L. Improvement of segmental mispronunciation detection with prior knowledge extracted from large L2 speech corpus. In: Proceeding of the 12th Annual Conference of the International Speech Communication Association. Florence, Italy: ISCA, 2011. 1593-1596
[5]  Meng H, Lo Y Y, Wang L, Lau W Y. Deriving salient learners' mispronunciations from cross-language phonological comparisons. In: Proceeding of the 2007 Automatic Speech Recognition and Understanding Workshop. Kyoto, Japan: IEEE, 2007. 437-442
[6]  Stanley T, Hacioglu K, Pellom B. Statistical machine translation framework for modeling phonological errors in computer assisted pronunciation training system. In: The 2011 Speech and Language Technology in Education Workshop. Venice, Italy: ISCA, 2011. 125-128
[7]  Stanley T, Hacioglu K. Improving L1-specific phonological error diagnosis in computer assisted pronunciation training. In: Proceeding of the 13th Annual Conference of the International Speech Communication Association. Portland, Oregon: ISCA, 2012. 826-829
[8]  Qian X J, Meng H, Soong F. Capturing L2 segmental mispronunciations with ioint-sequence models in computer-aided pronunciation training (CAPT). In: Proceeding of the 7th International Symposium on Chinese Spoken Language Processing. Taiwan, China: IEEE Computer Society, 2010. 84-88
[9]  Mohri M, Pereira F, Riley M. Weighted finite-state transducers in speech recognition. Computer Speech and Language, 2002, 16(1): 69-88
[10]  Bisani M, Ney H. Joint-sequence models for grapheme-to-phoneme conversion. Speech Communication, 2008, 50(5): 434-451
[11]  David T, Miles O. Randomised language modelling for statistical machine translation. In: Proceedings of the 45th Prague, Czech Republic Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL, 2007. 512-519
[12]  Oparin I, Sundermeyer M, Ney H, Gauvain J. Performance analysis of neural networks in combination with n-gram language models. In: Proceeding of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. Kyoto, Japan: IEEE, 2012. 5005-5008
[13]  Eskenazi M. An overview of spoken language technology for education. Speech Communication, 2009, 51(10): 823-844
[14]  Yoon S Y, Hasegawa-Johnson M, Sproat R. Landmark-based automated pronunciation error detection. In: Proceeding of the 11th Annual Conference of the International Speech Communication Association. Tokyo: ISCA, 2010. 614-617
[15]  Wei S, Hu G P, Hu Y, Wang R H. A new method for mispronunciation detection using support vector machine based on pronunciation space models. Speech Communication, 2009, 51(10): 896-905
[16]  Wang H C, Waple C J, Kawahara T. Computer Assisted language learning system based on dynamic question generation and error prediction for automatic speech recognition. Speech Communication, 2009, 51(10): 995-1005
[17]  Yuan H, Zhao J H, Liu J. A two-stage mispronunciation detection approach for computer-assisted pronunciation training. In: Proceeding of the Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2011. Xi'an, China: Asia-Pacific Signal and Information Processing Association, 2011. 972-976
[18]  Lo W K, Zhang S, Meng H. Automatic derivation of phonological rules for mispronunciation detection in a computer-assisted pronunciation training system. In: Proceeding of the 11th Annual Conference of the International Speech Communication Association. Makuhari, Chiba, Japan: ISCA, 2010. 765-768
[19]  Harrison A M, Lau W Y, Meng H, Wang L. Improving mispronunciation detection and diagnosis of learners' speech with context-sensitive phonological rules based on language transfer. In: Proceeding of the 9th Annual Conference of the International Speech Communication Association. Brisbane: ISCA, 2008. 2787-2790
[20]  Qian X J, Meng H, Soong F F. On mispronunciation lexicon generation using joint-sequence multigrams in computer-aided pronunciation training. In: Proceeding of the 12th Annual Conference of the International Speech Communication Association. Italy, Florence: ISCA, 2011. 865-868
[21]  Gass S M, Selinker L. Language Transfer in Language Learning. Philadelphia, USA: John Benjamins Publishing Company, 1993. 87-101
[22]  Harrison A M, Lo W K, Qian X J, Meng H. Implementation of an extended recognition network for mispronunciation detection and diagnosis in computer-assisted pronunciation training. In: The 2009 Speech and Language Technology in Education Workshop. Warwickshire, England: ISCA, 2009. 45-48
[23]  Schwenk H. Continuous space language models. Computer Speech and Language, 2007, 21(3): 492-518
[24]  Schwenk H. Continuous-space language models for statistical machine translation. The Prague Bulletin of Mathematical Linguistics, 2010, 93(1): 137-146

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