Abstract
The fastest and most straightforward way of communication for mankind is the voice. Therefore, the best way to interact with computers should be the voice too. That is why at the moment men are searching new ways to interact with computers. This interaction is improved if the words spoken by the speaker are organized in Natural Language.
In this article, it is proposed a model to recover information from databases through queries in Spanish Natural Language using the voice as the way of communication. This model incorporates a Hybrid Intelligent System based on Genetic Algorithms and a Kohonen Self-Organizing Map (SOM) to recognize the present phonemes in a word through time. This approach allows us to remake up a word with speaker independence. Furthermore, it is proposed the use of a compiler with type 2 grammar according to the Chomsky Hierarchy to support the syntactic and semantic structure in Spanish language. Our experiments suggest that the Spoken Natural Language improves notably the Human-Computer interaction when compared with traditional input methods such as: mouse or keybord.
Keywords
- Speech Recognition
- Human Computer Interaction
- Multi Layer Perceptron
- Speech Recognition System
- Learn Vector Quantization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Wickens, C.D., Hollands, J.G.: Engineering Psychology and Human Performance, 3rd edn. Prentice-Hall, Englewood Cliffs (Sept. 1999)
Laurel, B.: Interface agents: Metaphors with character, pp. 355–365 (1999)
Atal, B.: Automatic recognition of speakers from their voices. Proceedings of the IEEE 64, 460–475 (1976)
Hunt, M.: Spectral signal processing for asr (1999)
Gu, L., Rose, K.: Perceptual harmonic cepstral coefficients for speech recognition in noisy environment (2001)
Gales, M.: Model-based techniques for noise robust speech recognition (1996), Available: http://citeseer.ist.psu.edu/gales95modelbased.html
Schlüter, R., Ney, H.: Using phase spectrum information for improved speech recognition performance (1998)
Johnson, S., Jourlin, P., Moore, G., Jones, K.S., Woodland, P.: The cambridge university spoken document retrieval system. In: Proc ICASSP ’99, Phoenix, AZ, vol. 1, pp. 49–52 (1999), Available: http://citeseer.ifi.unizh.ch/johnson99cambridge.html
Hermansky, H., Morgan, N.: RASTA processing of speech. IEEE Transactions on Speech and Acoustics 2, 587–589 (1994)
Huang, X., Alleva, F., Hon, H.-W., Hwang, M.-Y., Rosenfeld, R.: The SPHINX-II speech recognition system: an overview. Computer Speech and Language 7(2), 137–148 (1993), citeseer.ifi.unizh.ch/huang92sphinxii.html
Kershaw, D.J.: Phonetic context-dependency in a hybrid ann/hmm speech recognition system (1996), Available: http://citeseer.ifi.unizh.ch/175909.html
Neto, J., Almeida, L., Hochberg, M., Martins, C., Nunes, L., Renals, S., Robinson, A.: Speakeradaptation for hybrid hmm-ann continuous speech recognition system (1995), Available: http://citeseer.ifi.unizh.ch/neto95speakeradaptation.html
Whitley, L.D., Dominic, S., Das, R.: Genetic reinforcement learning with multilayer neural networks. In: ICGA, pp. 562–569 (1991)
Miller, G.F., Todd, P.M., Hegde, S.U.: Designing neural networks using genetic algorithms. In: ICGA, pp. 379–384 (1989)
Romaniuk, S.G.: Evolutionary growth perceptrons. In: ICGA, pp. 334–341 (1993)
Huang, H., Acero, A., Hon, H.W.: Spoken Language Processing - A Guide to Theory, Algorithms and Systems Development. Prentice-Hall, Englewood Cliffs (2001)
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Florez-Choque, O., Cuadros-Vargas, E. (2007). An Improve to Human Computer Interaction, Recovering Data from Databases Through Spoken Natural Language. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_74
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DOI: https://doi.org/10.1007/978-3-540-72393-6_74
Publisher Name: Springer, Berlin, Heidelberg
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