A Study on the Performance Evaluation of Machine Learning Models for Phoneme Classification-Reference-Cited by-同舟云学术

A Study on the Performance Evaluation of Machine Learning Models for Phoneme Classification

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the 2019 11th International Conference on Machine Learning and Computing - ICMLC '19
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Author:

Imran Ali Shariq¹,Shahrebabaki Abdolreza Sabzi¹,Olfati Negar¹,Svendsen Torbjørn¹

Affiliation:

1. Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Publisher

ACM Press

Reference21 articles.

1. P. Schwarz, P. Matejka, L. Burget, and O. Glembek, "Phoneme recognizer based on long temporal context," Speech Processing Group, Faculty of Information Technology, Brno University of Technology.[Online]. Available: http://speech.fit.vutbr.cz/en/software, 2006.

2. H. Hermansky and S. Sharma, "Temporal patterns (TRAPs) in ASR of noisy speech," in IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99, vol. 1, pp. 289--292, March 1999.

3. A. Mohamed, G. E. Dahl, and G. Hinton, "Acoustic modeling using deep belief networks," Transsactions on Audio, Speech and Language Processing, vol. 20, pp. 14--22, January 2012.

4. G. E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition," IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 1, pp. 30--42, 2012.

5. B. Kingsbury, T. N. Sainath, and H. Soltau, "Scalable minimum bayes risk training of deep neural network acoustic models using distributed hessian-free optimization," in 13th Annual Conference of the International Speech Communication Association (InterSpeech 2012), pp. 10--13, ISCA, September 2012.