A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition

Abstract

About one-quarter of all car collisions in the United States are caused by distracted driving, and this ratio is expected to rise. As vehicles are equipped with more elaborate and complex technology, human-vehicle interaction via dashboard displays and controls will become more complex and distracting. Human-vehicle interaction via voice-based technology offers a less distracting alternative. In this study we aim to develop a voice-based car assistant, with a focus on Arabic language speech recognition. We prepare a new 4000-word domain-specific lexicon to comprehensively support driver-vehicle interactions, and we create corresponding text and speech corpora. Then we extract acoustic feature vectors and use various acoustic models to support speech recognition. The language model is created using an n-gram model. Then acoustic and language models, and the lexicon are combined to generate a decoding graph. The text corpus consists of 6110 elements, including words, phrases, and sentences. The speech corpus has more than 60000 recordings (almost 50 hours). For the decoding of noise-free audio, a Deep Neural Network + Hidden Markov Model provided 94.832% accuracy, a Subspace Gaussian Mixture Model + Hidden Markov Model provided 94.2% accuracy, and the best Gaussian Mixture Model + Hidden Markov Model provided 94.13% accuracy. For the decoding of noisy audio, a Deep Neural Network + Hidden Markov Model provided 93.316% accuracy, a Subspace Gaussian Mixture Model + Hidden Markov Model provided 92.62% accuracy, and the best Gaussian Mixture Model + Hidden Markov Model provided 91.82% accuracy. A usability study was conducted on the system with 10 participants. Almost all of the results of that study showed usability ratings of greater than 4.0 out of 5.0. These usability ratings indicate that the proposed system was seen by the participants as important, and useful for reducing driver distraction.

Intelligent Driver Model-Based Vehicular Ad Hoc Network Communication in Real-Time Using 5G New Radio Wireless Networks

“…The transmission bandwidth closest to the base station is assumed to be b comm (occurring at a distance from the base station). If the distance between the vehicle and the base station is x distance, according to the characteristic that the signal quality will be inversely proportional to the square of the transmission distance, we set the expected bandwidth (EB), i.e., EB 5G of the 5G NR system as (5). After the above parameters are given, this paper proposes a method to calculate the expected bandwidth value EB V AN ET of packets transmitted through the VANETs system and the expected bandwidth value EB 5G of the 5G NR system, respectively.…”

“…For future assistance and automation systems, V2V and V2I, both together called vehicle-2-everything (V2X) communication, will be the underlying technology to develop and deploy a new class of assistance systems, which exchanges information like recommendations, warnings, and status information that vehicles could not gather on their own. Gupta et al [4] established an indoor rapid test platform for autonomous vehicles based on vehicle-in-the-loop; Jaradat et al [5] proposed and developed a human-vehicle road interaction mode virtual simulation system and driving data acquisition system, and realized the human-computer interaction and dynamic display of information in the simulation system. The vehicle industry, academia, and government agencies have jointly endeavored to realize the concept of vehicular communications [1], [6]; this field attracts researchers from different fields of development for applications, protocols, and simulation tools for VANET.…”

To Explore the Development Status and Future Development Direction of Intelligent and Connected Vehicles

“…Intelligent voice interaction technology is already integrated into the in-vehicle assistive devices of smart connected cars for Arab drivers [13] .…”

Building Python Application for Webmail Interfaces Navigation using Voice Recognition Technology

“…Moreover, ASR has already been integrated by applications and AT developers, enabling individuals to control wheelchairs and access computers using only AVC [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. This advancement enhances accessibility and empowers Arabic-speaking individuals, especially those with disabilities, to perform everyday tasks independently.…”