An Improve to Human Computer Interaction, Recovering Data from Databases Through Spoken Natural Language
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.
KeywordsSpeech Recognition Human Computer Interaction Multi Layer Perceptron Speech Recognition System Learn Vector Quantization
Unable to display preview. Download preview PDF.
- 1.Wickens, C.D., Hollands, J.G.: Engineering Psychology and Human Performance, 3rd edn. Prentice-Hall, Englewood Cliffs (Sept. 1999)Google Scholar
- 2.Laurel, B.: Interface agents: Metaphors with character, pp. 355–365 (1999)Google Scholar
- 4.Hunt, M.: Spectral signal processing for asr (1999)Google Scholar
- 5.Gu, L., Rose, K.: Perceptual harmonic cepstral coefficients for speech recognition in noisy environment (2001)Google Scholar
- 6.Gales, M.: Model-based techniques for noise robust speech recognition (1996), Available: http://citeseer.ist.psu.edu/gales95modelbased.html
- 7.Schlüter, R., Ney, H.: Using phase spectrum information for improved speech recognition performance (1998)Google Scholar
- 8.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
- 9.Hermansky, H., Morgan, N.: RASTA processing of speech. IEEE Transactions on Speech and Acoustics 2, 587–589 (1994)Google Scholar
- 11.Kershaw, D.J.: Phonetic context-dependency in a hybrid ann/hmm speech recognition system (1996), Available: http://citeseer.ifi.unizh.ch/175909.html
- 12.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
- 13.Whitley, L.D., Dominic, S., Das, R.: Genetic reinforcement learning with multilayer neural networks. In: ICGA, pp. 562–569 (1991)Google Scholar
- 14.Miller, G.F., Todd, P.M., Hegde, S.U.: Designing neural networks using genetic algorithms. In: ICGA, pp. 379–384 (1989)Google Scholar
- 15.Romaniuk, S.G.: Evolutionary growth perceptrons. In: ICGA, pp. 334–341 (1993)Google Scholar
- 16.Huang, H., Acero, A., Hon, H.W.: Spoken Language Processing - A Guide to Theory, Algorithms and Systems Development. Prentice-Hall, Englewood Cliffs (2001)Google Scholar