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文章基本信息

  • 标题:Improving Language Models in Speech-Based Human-Machine Interaction
  • 作者:Raquel Justo ; Oscar Saz ; Antonio Miguel
  • 期刊名称:International Journal of Advanced Robotic Systems
  • 印刷版ISSN:1729-8806
  • 电子版ISSN:1729-8814
  • 出版年度:2013
  • 卷号:10
  • 期号:2
  • 页码:87
  • DOI:10.5772/55407
  • 语种:English
  • 出版社:SAGE Publications
  • 摘要:This work focuses on speech-based human-machine interaction. Specifically, a Spoken Dialogue System (SDS) that could be integrated into a robot is considered. Since Automatic Speech Recognition is one of the most sensitive tasks that must be confronted in such systems, the goal of this work is to improve the results obtained by this specific module. In order to do so, a hierarchical Language Model (LM) is considered. Different series of experiments were carried out using the proposed models over different corpora and tasks. The results obtained show that these models provide greater accuracy in the recognition task. Additionally, the influence of the Acoustic Modelling (AM) in the improvement percentage of the Language Models has also been explored. Finally the use of hierarchical Language Models in a language understanding task has been successfully employed, as shown in an additional series of experiments.
  • 关键词:Human-machine Interaction; Automatic Speech Recognition; Language Understanding; Language Modelling; Classes Made Up of Phrases
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