Hindi Dialect (Bangro) Spoken Language Recognition (HD-SLR) System Using Sphinx3

Author:

Virender Kadyan ,Amitoj Singh ,Parth Wadhwa

Publisher

Springer Singapore

Reference7 articles.

1. Huggins-Daines, D., Kumar, M., Chan, A., Black, A. W., Ravishankar, M., and Rudnicky, A. “Pocketsphinx: A free, real-time continuous speech recognition system for hand-held devices.” IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1. IEEE, (2006) 1–1.

2. Rao, K. S., and Koolagudi, S. G. “Identification of Hindi dialects and emotions using spectral and prosodic features of speech”. IJSCI: International Journal of Systemics, Cybernetics and Informatics 9.4 (2011) 24–33.

3. Sinha, S., Jain, A., and Agrawal, S. S. “Speech processing for Hindi dialect recognition.” Advances in Signal Processing and Intelligent Recognition Systems. Springer International Publishing, (2014) 161–169.

4. Huang, R., Hansen, J. H., n Angkititrakul, P. “Dialect/accent classification using unrestricted audio.” Audio, Speech, IEEE Transactions and Language Processing, 15(2) (2007) 453–464.

5. Liu, M., Xu, B., Hunng, T., Deng, Y., and Li, C. “Mandarin accent adaptation based on context-independent/context-dependent pronunciation modeling.” IEEE International Conference, Acoustics, Speech, and Signal Processing. ICASSP. Proceedings Vol. 2. IEEE, (2000) 1025–1028.

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