Mispronunciation detection and diagnosis in l2 English speech using multi-distribution Deep Neural Networks

Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014

Volumn , Issue , 2014, Pages 255-259

  Li, Kun ^a   Meng, Helen ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

deep neural networks; L2 English speech; mispronunciation detection and diagnosis; speech recognition

Indexed keywords

DEEP NEURAL NETWORKS; SPEECH; TELEPHONE SETS;

ACOUSTIC FEATURES; ACOUSTIC MODEL; CONTEXTUAL INFORMATION; INPUT FEATURES; MISPRONUNCIATION DETECTIONS; PHONOLOGICAL MODELING; TARGET WORDS; TRAINING DATA;

SPEECH RECOGNITION;

EID: 84912101692     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCSLP.2014.6936724     Document Type: Conference Paper

References (18)

1. Lee, K-F and Hon, H-W, "Speaker-independent phone recognition using hidden Markov models", IEEE Trans. on Audio, Speech and Language Proc., 1989.
2. Hinton, G. E., Osindero, S. and Teh, Y., "A fast learning algorithm for deep belief nets", Neural Computation, 18:1527-1554, 2006.
3. Hinton, G. E. and Salakhutdinov, R. R., "Reducing the Dimensionality of Data with Neural Networks", Science, 313:504-507, 2006.
4. Mohamed, A., Dahl, G. E. and Hinton G. E., "Acoustic modeling using deep belief networks", IEEE Trans. on Audio, Speech and Language Proc., 20(1):14-22, 2012.
5. Dahl, G. E., Yu, D., Deng, L. and Acero A., "Context-dependent pre-trained deep neural networks for large vocabulary speech recognition", IEEE Trans. on Audio, Speech and Language Proc., 20(1):30-40, 2012.
6. Mohamed, A., Sainath, T. N., Dahl, G., Ramabhadran, B., Hinton, G. E. and Picheny, M.A."Deep belief networks using discriminative features for phone recognition", ICASSP 2011.
7. Abdel-Hamid, O., Mohamed, A., Jiang, H. and Penn, G., "Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition", ICASSP 2012.
8. Deng L., Abdel-Hamid, O. and Yu D., "A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion", ICASSP 2013.
9. Graves, A., Mohamed A. and Hinton G. E., "Speech Recognition with Deep Recurrent Neural Networks", ICASSP 2013.
10. Harrison, A. M., Lo, W., Qian, X. and Meng, H., "Implementation of an Extended Recognition Network for Mispronunciation Detection and Diagnosis in Computer-Assisted Pronunciation Training", SLaTE 2009.
11. Lo W., Zhang S. and Meng H., "Automatic Derivation of Phonological Rules for Mispronunciation Detection in a Computer-Assisted Pronunciation Training System, " Interspeech 2010.
12. Qian X., Soong F. and Meng, H., "Discriminative Acoustic Model for Improving Mispronunciation Detection and Diagnosis in Computer-Aided Pronunciation Training (CAPT), " Interspeech 2010.
13. Qian, X., Meng, H. and Soong, F., "On Mispronunciation Lexicon Generation using Joint-sequence Multigrams in Computer-Aided Pronunciation Training (CAPT)", Interspeech 2011.
14. Qian, X., Meng, H. and Soong, F., "The use of DBN-HMMs for mispronunciation detection and diagnosis in L2 English to support computer-aided pronunciation training", Interspeech 2012.
15. Kang, S., Qian, X. and Meng, H., "Multi-distribution deep belief network for speech synthesis", ICASSP 2013.
16. Li, K., Qian, X., Kang, S. and Meng, H., "Lexical Stress Detection for L2 English Speech Using Deep Belief Networks", Interspeech 2013.
17. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors", Nature, vol. 323, no. 6088, pp. 533-536, 1986
18. Yong, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., and Woodland, P., "The HTK book (for HTK version 3.4)", Cambridge university, 187-189, 2006.