Title: Research on the interactive design of electric vehicle interior based on voice sensing and visual imagery

Authors: Tao Ba; Shan Li; Ying Gao; Diyuan Tan

Addresses: School of Art and Design, Zhengzhou University of Light Industry, Zhengzhou, Henan, China ' College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou, Henan, China ' School of Art and Design, Zhengzhou University of Light Industry, Zhengzhou, Henan, China ' School of Electro-mechanical Engineering, Zhongyuan Institute of Science and Technology, Xuchang, Henan, China

Abstract: With the complete function of modern automobiles, in-vehicle intelligent devices are becoming more and more complex and the requirements for human-computer interaction are also increasing. The research proposes a speech recognition method that combines multi-window estimation spectral subtraction and dynamic time warping to enhance the denoising ability and speech recognition ability of in-vehicle devices. It also proposes actions based on a Gaussian hybrid segmentation algorithm and a visual image functional space segmentation algorithm. The automatic identification method and the validity of the algorithm are verified. The results show that under different input signal-to-noise ratios, the denoising capability of the method is improved by 2.45% to 31.47% over the baseline method. And the accuracy of speech recognition in the vehicle environment is 92.3% to 98.7%. It is hoped that this research can make some contributions to the upgrading of voice and visual interaction within electric vehicles.

Keywords: speech recognition; multi-window spectral subtraction; dynamic time warping; visual imagery; space segmentation.

DOI: 10.1504/IJVICS.2023.131613

International Journal of Vehicle Information and Communication Systems, 2023 Vol.8 No.1/2, pp.152 - 169

Received: 21 Nov 2022
Accepted: 29 Jan 2023
Published online: 20 Jun 2023 *