Voice enabled smart drone control

Fayjie, Abdur Razzaq; Rumman, Amir; Doukhi, Oualid; Lee, Deok Jin

doi:10.1109/icufn.2017.7993759

Cited by 14 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy of PocketSphinx system reached only 79,2 % with similar parameters during training of systems [23], [24]. This is primarily due to use of various neural network topologies.…”

Section: Results Of Experimental Research and Comparison With Analoguesmentioning

confidence: 95%

Recognition of Voice Commands Based on Neural Network

Lyashenko¹,

Laariedh²,

Sotnik³

et al. 2021

TEM Journal

View full text Add to dashboard Cite

The paper considers features of voice commands pronunciation models, namely, dependence: system accuracy on number of states for phonemes; system accuracy on learning rate; accuracy of system on value of training set. A speech recognition system based on neural networks is proposed. A speech recognition system is not easy to implement and requires an understanding of speech recognition basics. The developed system is compared with Speech Recognition from Google and Pocket Sphinx. The proposed system can recognize voice commands with an accuracy of 84.4 %.

show abstract

“…Accuracy of PocketSphinx system reached only 79,2 % with similar parameters during training of systems [23], [24]. This is primarily due to use of various neural network topologies.…”

Section: Results Of Experimental Research and Comparison With Analoguesmentioning

confidence: 95%

Recognition of Voice Commands Based on Neural Network

Lyashenko¹,

Laariedh²,

Sotnik³

et al. 2021

TEM Journal

View full text Add to dashboard Cite

show abstract

“…A more accurate speech recognition method was presented by Fayjie et al [33]. In that study, the authors used a hidden Markov model for speech recognition with voice adaptation in order to control the UAV.…”

Section: Speech Controlmentioning

confidence: 99%

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

2020

View full text Add to dashboard Cite

Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and extend to many areas of society, including the industrialized world. A common alternative for controlling the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices are used. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this research, we experimented with a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control was performed in a more natural, human-like way to communicate the instructions. Moreover, we implemented an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. We conducted experiments involving participants giving voice commands to the drone in both languages in order to compare the effectiveness of each, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion were applied to the voice commands to test the proposed approach when it encountered noisy input signals. The results obtained showed that the unmanned aerial vehicle was capable of interpreting user voice instructions. Speech-to-action recognition improved for both languages with phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions, respectively. However, with our phoneme matching approach the results are improved, yielding 93.33% accuracy for English and 100.00% accuracy for Spanish.

show abstract

“…Moreover, the human movements (gestures), voices and brain waves based wireless RC are developed to overcome the problem [14]. The voice-based control system has been proposed by [15], the work presented an AI based voice recognition to control a UAV. Another mobile robot control can be seen in [16], the system proposed a remote control for a mobile robot based on android application which integrated with web-page.…”

Section: Introductionmentioning

confidence: 99%

HoloGCS : Mixed Reality-based Ground Control Station for Unmanned Aerial Vehicle

Widiyanti¹,

Asmoro²,

Shin³

2022

Preprint

View full text Add to dashboard Cite

Ground control station (GCS) is a system for controlling and monitoring unmanned aerial vehicle (UAV). In current GCS, the device used are considered as complex environment. This paper proposes a video streaming and speech command control for supporting mixed reality based UAV GCS using Microsoft HoloLens. Video streaming will inform the UAV view and transmit the raw video to the HoloLens, while the HoloLens steers the UAV based on the displayed UAV field of view (FoV). Using the HoloLens Mixed Reality Tool-Kit (MRTK) speech input, UAV speech control from the HoloLens was successfully implemented. Finally, experimental results based on video streaming and speech command calculation of the throughput, round-time trip, latency and speech accuracy tests are discussed to demonstrate the feasibility of the proposed scheme.

show abstract

Voice enabled smart drone control

Cited by 14 publications

References 3 publications

Recognition of Voice Commands Based on Neural Network

Recognition of Voice Commands Based on Neural Network

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

HoloGCS : Mixed Reality-based Ground Control Station for Unmanned Aerial Vehicle

Contact Info

Product

Resources

About