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Stochastic Modelling of Sentence Semantics in Speech Recognition

  • Włodzimierz Kasprzak
  • Paweł Przybysz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

A stochastic approach to spoken sentence recognition is proposed for the purpose of an automatic voice-based dialogue system. Three main tasks are distinguished: word recognition, word chain filtering and sentence recognition. The first task is solved by typical acoustic processing followed by phonetic word recognition with the use of Hidden Markov Models (HMM) and Viterbi search. For the second solution an N-gram model of natural language is applied and a token-passing search is designed for the filtering of important word chains. The third task is solved due to a semantic HMM of sentences. The final sentence is recognized and a meaning is assigned to its elements with respect to given application domain. A particular spoken sentence recognition system has been implemented for train connection queries.

Keywords

Hide Markow Model Word Recognition Speech Recognition Language Model Speech Recognition System 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Włodzimierz Kasprzak
    • 1
  • Paweł Przybysz
    • 1
  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarszawaPoland

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