IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 11    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 11, November 2011
Recurrent Neural Prediction Model for Digits Recognition
Full Text(PDF, 3000)  PP.  
Tellez Paola
Recurrent neural network, speech recognition, optimization, prediction
There are three models of recurrent neural networks that can be used for speech recognition, but we do not know which one is the best suited for Spanish digits recognition. Each language uses different parts of the mouth to create sounds, so it is logical that we develop different parts of the brain to recognize these sounds. The best recurrent neural network to recognize Spanish digits is Jordan recurrent neural network. The next step is to improve the performance of Jordan recurrent neural network for Spanish recognition.
[1] Speaker-independent word recognition using a neural predic-tion model. Watanabe, Ken-ichi Iso and Takao. s.l. : IEEE Proc. ICASSP, 1990, Vol. 8, pp. 441-444.

[2] Phoneme recognition using time-delay neural netwok. A. Wai-bel, T. Hanazawa, G. Hinton and K. Shikano. s.l. : IEEE Transac-tions Acoustics, Speech, Signal Processing., 1989, Vol. 37, pp. 328-339.

[3] Recurrent Neural networks. Haruhisha, Takahashi. s.l. : SP93-111, 1993.

[4] Finding structure in time. Elman, J.L. s.l. : Cognitive Science, 1990, Vol. 14, pp. 179-211.

[5] Spoken arabic digits recognizer using recurrent neural net-works. Ajami, Yousef. s.l. : Proceedings of the 4th IEEE Interna-tional Symposium, 2004, pp. 195-199.

[6] Jordan, M.I. Serial Order: a parallel distributed processing ap-proach. Institute for Cognitive Science, Report 8604. University of California, San Diego.

[7] Japanese digits recognition using recurrent neural prediction model. Uchiyama Toru, Takahashi Haruhisha. s.l. : Tech report of Universidad de Electrocomunicaciones , 1999, Vols. J82-D-II, pp. 1-7.

Untitled Page