Voice Control Framework for Form Based Applications

  • Ionut Cristian Paraschiv
  • Mihai Dascalu
  • Stefan Trausan-Matu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8722)


Enabling applications with natural language processing capabilities facilitates user interaction, especially in the case of complex applications such as a mobile banking. In this paper we introduce the steps required for building such a system, starting from the presentation of different alternatives alongside their problems and benefits, and ending up with integrating them within our implemented system. However, one of the main problems with voice recognition models is that they tend to use different approximations and thresholds that aren’t completely reliable; therefore, the best solution consists of combining multiple approaches. Consequently, we opted to implement two different and complementary recognition models, and to detail in the end how their integration within the framework’s architecture leads to encouraging results.


Voice recognition Natural Language Processing Sentence similarity Internet banking application Discourse analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jurafsky, D., Martin, J.H.: An introduction to Natural Language Processing. Computational linguistics, and speech recognition. Pearson Prentice Hall, London (2009)Google Scholar
  2. 2.
    Pucella, R., Chong, S.: A Framework for Creating Natural Language User Interfaces for Action-Based Applications. In: 3rd Int. AMAST Workshop on Algebraic Methods in Language Processing, TWLT Report 21, pp. 83–98 (2003)Google Scholar
  3. 3.
    Li, Y., Mclean, D., Bandar, Z.A., O’Shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering 18(8), 1138–1150 (2006)CrossRefGoogle Scholar
  4. 4.
    Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104(2), 211–240 (1997)CrossRefGoogle Scholar
  5. 5.
    Budanitsky, A., Hirst, G.: Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32(1), 13–47 (2006)CrossRefzbMATHGoogle Scholar
  6. 6.
    Nuance Communications: Dragon Software Developer Kits,
  7. 7.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In: HLT-NAACL 2003, pp. 252–259. ACL, Edmonton (2003)Google Scholar
  8. 8.
    Dascălu, M.: Analyzing Discourse and Text Complexity for Learning and Collaborating. SCI, vol. 534. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  9. 9.
    Dascalu, M., Dessus, P., Bianco, M., Trausan-Matu, S., Nardy, A.: Mining texts, learners productions and strategies with ReaderBench. In: Peña-Ayala, A. (ed.) Educational Data Mining. SCI, vol. 524, pp. 345–377. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ionut Cristian Paraschiv
    • 1
  • Mihai Dascalu
    • 1
  • Stefan Trausan-Matu
    • 1
    • 2
  1. 1.Computer Science DepartmentUniversity Politehnica of BucharestRomania
  2. 2.Research Institute for Artificial Intelligence of the Romanian AcademyRomania

Personalised recommendations