Voice command controlled mobile vehicle application

Iskender, Ahmet; Üçgün, Hakan; Yüzgeç, Uğur; Kesler, Metin

doi:10.1109/ubmk.2017.8093565

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…As we described in Introduction, the methods for controlling voice devices rely primarily on an existing speech recognition system due to the infeasible nature of the development and installation of a speech recognition module of high accuracy. Among the speech recognition systems available, recent studies have used the GSR (Google speech recognition) service because of its wide availability on Android devices and the ability to integrate it into device driver programs; examples include a study by Aripin et al [4] (they built a control system for indoor devices such as lamps and fans to support the elderly and the disabled) and one by Iskender et al [5] (they used the same method to operate vehicles). However, these studies are not public datasets.…”

Section: Discussionmentioning

confidence: 99%

“…They suggested GSR to recognize voice commands that would then be transmitted via Bluetooth to control the devices. Iskender et al [5] proposed the same method to operate vehicles, and Guo et al [6] used this method to build a spelling correction model.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we developed a method of Vietnamese voice command recognition using GSR services, as in the above studies [4,5]. We chose this automatic speech recognition (ASR) system because it is supported on all mobile devices.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Recognizing Vietnamese Voice Commands on Smartphones with Support Vector Machine and Convolutional Neural Networks

Nguyen

Cao

2020

Wireless Communications and Mobile Computing

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This paper will present a new method of identifying Vietnamese voice commands using Google speech recognition (GSR) service results. The problem is that the percentage of correct identifications of Vietnamese voice commands in the Google system is not high. We propose a supervised machine-learning approach to address cases in which Google incorrectly identifies voice commands. First, we build a voice command dataset that includes hypotheses of GSR for each corresponding voice command. Next, we propose a correction system using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that the correction system reduces errors in recognizing Vietnamese voice commands from 35.06% to 7.08% using the SVM model and 5.15% using the CNN model.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Recognizing Vietnamese Voice Commands on Smartphones with Support Vector Machine and Convolutional Neural Networks

Nguyen

Cao

2020

Wireless Communications and Mobile Computing

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“…A robot using HRI and detecting the faces using OpenCV algorithms using ROS is proposed in [25]. In the interaction tasks of navigation and speech, İskender et al [26] proposes a method to control the vehicle autonomously using speech commands. Here the VoiceBot application and google voice were used for recognizing speech commands.…”

Section: Introductionmentioning

confidence: 99%

Robot operating system based autonomous navigation platform with human robot interaction

Megalingam

Naick²,

Motheram³

et al. 2023

TELKOMNIKA

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In emerging technologies, indoor service robots are playing a vital role for people who are physically challenged and visually impaired. The service robots are efficient and beneficial for people to overcome the challenges faced during their regular chores. This paper proposes the implementation of autonomous navigation platforms with human-robot interaction which can be used in service robots to avoid the difficulties faced in daily activities. We used the robot operating system (ROS) framework for the implementation of algorithms used in auto navigation, speech processing and recognition, and object detection and recognition. A suitable robot model was designed and tested in the Gazebo environment to evaluate the algorithms. The confusion matrix that was created from 125 different cases points to the decent correctness of the model.

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