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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Robotics

Background:

  • Traditional chatbots primarily process sound signals, neglecting crucial visual information in conversations.
  • This limitation can lead to misunderstandings and inaccurate responses in complex interaction scenarios.

Purpose of the Study:

  • To develop a multi-sensor context-aware chatbot technology that incorporates both image and sound data.
  • To improve the accuracy and reduce misunderstandings in chatbot responses by leveraging multimodal input.

Main Methods:

  • Utilized a recurrent neural network (RNN) architecture for the chatbot model.
  • Integrated image features extracted using a VGG16 model with sound signals.
  • Replaced Long Short-Term Memory (LSTM) with Gated Recurrent Unit (GRU) for enhanced performance.

Main Results:

  • Experimental results confirmed that integrating sound and image information significantly benefits the chatbot model.
  • The proposed chatbot demonstrated improved performance in understanding and responding to users in a companion robot context.
  • The feasibility of the multimodal approach was validated through empirical testing.

Conclusions:

  • Multimodal input, combining sound and image data, is crucial for developing more robust and context-aware conversational AI.
  • The proposed RNN-based chatbot with GRU and VGG16 feature extraction offers a promising solution for overcoming limitations of traditional sound-based systems.