Long short-term memory recurrent neural network architectures for Urdu acoustic modeling

Author:

Zia TehseenORCID,Zahid Usman

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Reference36 articles.

1. Ahad, A., Fayyaz, A., & Mehmood, T. (2002). Speech recognition using multilayer perceptron. In Proceedings of IEEE students conference (Vol. 1, pp 103–109).

2. Ali, H., Ahmad, N., & Hafeez, A. (2016). Urdu speech corpus and preliminary results on speech recognition. In International conference on engineering applications of neural networks (pp 317–325). New York: Springer.

3. Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., & Chen, J. (2016). Deep speech 2: End-to-end speech recognition in English and Mandarin. In International conference on machine Learning (pp 173–182).

4. Ashraf, J., Iqbal, N., Khattak, N. S., & Zaidi, A. M. (2010). Speaker independent Urdu speech recognition using HMM. In 7th IEEE international conference on informatics and systems (INFOS) (pp 1–5).

5. Azam, S. M., Mansoor, Z. A., Mughal, M. S., & Mohsin, S. (2007). Urdu spoken digits recognition using classified MFCC and backpropgation neural network. In IEEE conference on computer graphics, imaging and visualisation (pp 414–418).

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