Speech Emotion Recognition Enhanced Traffic Efficiency Solution for Autonomous Vehicles in a 5G-Enabled Space-Air-Ground Integrated Intelligent Transportation System

Liang Tan, Keping Yu, Long Lin, Xiaofan Cheng, Gautam Srivastava, Jerry Chun Wei Lin, Wei Wei

Research output: Contribution to journalArticlepeer-review

Abstract

Speech emotion recognition (SER) is becoming the main human-computer interaction logic for autonomous vehicles in the next generation of intelligent transportation systems (ITSs). It can improve not only the safety of autonomous vehicles but also the personalized in-vehicle experience. However, current vehicle-mounted SER systems still suffer from two major shortcomings. One is the insufficient service capacity of the vehicle communication network, which is unable to meet the SER needs of autonomous vehicles in next-generation ITSs in terms of the data transmission rate, power consumption, and latency. Second, the accuracy of SER is poor, and it cannot provide sufficient interactivity and personalization between users and vehicles. To address these issues, we propose an SER-enhanced traffic efficiency solution for autonomous vehicles in a 5G-enabled space-air-ground integrated network (SAGIN)-based ITS. First, we convert the vehicle speech information data into spectrograms and input them into an AlexNet network model to obtain the high-level features of the vehicle speech acoustic model. At the same time, we convert the vehicle speech information data into text information and input it into the Bidirectional Encoder Representations from Transformers (BERT) model to obtain the high-level features of the corresponding text model. Finally, these two sets of high-level features are cascaded together to obtain fused features, which are sent to a softmax classifier for emotion matching and classification. Experiments show that the proposed solution can improve not only the SAGIN's service capabilities, resulting in a large capacity, high bandwidth, ultralow latency, and high reliability, but also the accuracy of vehicle SER as well as the performance, practicality, and user experience of the ITS.

Original languageEnglish
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • 5G-enabled SAGIN
  • artificial intelligence
  • Autonomous vehicles
  • autonomous vehicles
  • Communication networks
  • Computer science
  • Emotion recognition
  • ITS.
  • Next generation networking
  • Satellites
  • Speech emotion recognition
  • Speech recognition

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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