Robot speech learning via entropy guided LVQ and memory association

Q. Liu, S. Levinson, Y. Wu, T. Huang

Research output: Contribution to conferencePaperpeer-review


The goal of this project is to teach a computer-robot system to understand human speech through natural human-computer interaction. To achieve this goal, we develop an interactive and incremental learning algorithm based on entropy-guided LVQ and memory association. Supported by this algorithm, the robot has the potential to learn unlimited sounds progressively. Experimental results of a multilingual short-speech learning task are given after the presentation of the learning system. Further investigation of this learning system will include human-computer interactions that involve more modalities, and applications that use the proposed idea to train home appliances.

Original languageEnglish (US)
Number of pages6
StatePublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001


OtherInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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