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A Real-time Voice Interface for Intelligent Wheelchairs

A Real-time Voice Interface for Intelligent Wheelchairs

S. Moschopoulos, I. Fudos, K. Koritsoglou, G. Tatsis, and Dimitrios Tzovaras

IMET (pp. 19-22), 2023

This paper reports on the development of a real-time voice interface for navigation purposes of electric wheelchairs. To this end, we employ a convolutional neural network trained and fine-tuned using a small dataset that consists of Greek commands. Furthermore, the study explores a highly quantized version of the network to achieve computational efficiency while maintaining high accuracy on an edge device. The experimental results confirm the effectiveness of the model in accurately detecting keywords in real time with minimal misclassifications.

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Abstract

This paper reports on the development of a real-time voice interface for navigation purposes of electric wheelchairs. To this end, we employ a convolutional neural network trained and fine-tuned using a small dataset that consists of Greek commands. Furthermore, the study explores a highly quantized version of the network to achieve computational efficiency while maintaining high accuracy on an edge device. The experimental results confirm the effectiveness of the model in accurately detecting keywords in real time with minimal misclassifications.

BibTeX Citation

@inproceedings{moschopoulos2023real,
  title={A Real-time Voice Interface for Intelligent Wheelchairs},
  authors="Moschopoulos, Spyros and Fudos, Ioannis and Koritsoglou, Kyriakos and Tatsis, Giorgos and Tzovaras, Dimitrios
  booktitle="IMET 2023", 
  pages="19-22",
  year="2023"
}