Enhancing accuracy of long contextual dependencies for Punjabi speech recognition system using deep LSTM

Author:

Kadyan Virender,Dua Mohit,Dhiman Poonam

Publisher

Springer Science and Business Media LLC

Subject

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

Reference43 articles.

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2. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265–283).

3. Aggarwal, R. K., & Dave, M. (2013). Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system. Telecommunication Systems, 52(3), 1457–1466.

4. Bahl, L., Brown, P., De Souza, P., & Mercer, R. (1986, April). Maximum mutual information estimation of hidden Markov model parameters for speech recognition. In ICASSP'86. IEEE international conference on acoustics, speech, and signal processing (Vol. 11, pp. 49–52). IEEE.

5. Bassan, N., & Kadyan, V. (2018). An experimental study of continuous automatic speech recognition system using MFCC with Reference to Punjabi. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, 1, 267.

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