Title: Multimodal speech-based human-computer interaction in automotive systems with integrated service recommendation

Authors: Enlin Xie

Addresses: Department of Culture and Communication, Xiangsihu College of Guangxi Minzu University, Nanning 530000, China

Abstract: Human-computer interaction plays a key role in intelligent automobile systems, and the current system has deficiencies in multimodal fusion, interaction accuracy, and real-time performance, which makes it difficult to meet the user experience requirements. For this reason, this paper proposes a car service recommendation method based on an improved collaborative filtering algorithm. Then, based on the recommended multimodal car service information, text and speech features are extracted using bidirectional long short-term memory (BiLSTM) and the Transformer, respectively. A low-rank multimodal integration approach is proposed to integrate the text and speech features. Modal redundancy and fault tolerance strategies are designed to enhance the system's decision-making capability. The experimental outcome demonstrates that the accuracy of human-computer interaction for the proposed method is 96.21%, and the time consumed for a single interaction is 17 ms, which is superior to the comparative methods and significantly improves driving safety and comfort.

Keywords: automotive speech recognition; human-computer interaction; service recommendation; collaborative filtering; CF; multimodal feature fusion.

DOI: 10.1504/IJSN.2025.148973

International Journal of Security and Networks, 2025 Vol.20 No.3, pp.164 - 174

Received: 28 Jun 2025
Accepted: 17 Jul 2025
Published online: 06 Oct 2025 *

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