Method of voice control functions of the UAV

Lavrynenko, Oleksandr; Konakhovych, G. F.; Bakhtiiarov, Denys

doi:10.1109/msnmc.2016.7783103

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…Currently, some researchers addressed UAV control using more natural interfaces for people, e.g., through automatic speech recognition. For instance, Lavrynenko et al [32] proposed a radio-based remote control system, in which a semantic identification method based on mel-frequency cepstral (MFC) coefficients was used. The audio captured was translated to an action that, in turn, was transferred to the UAV for its execution.…”

Section: Speech Controlmentioning

confidence: 99%

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Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

2020

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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.

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Section: Speech Controlmentioning

confidence: 99%

“…One of the most recent studies was an extension of [32] developed by Lavrynenko et al [37]. In this extension, the authors presented a similar radio-based control system with cepstral analysis.…”

Section: Speech Controlmentioning

confidence: 99%

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

2020

View full text Add to dashboard Cite

show abstract

“…Currently, some works have addressed UAV control using more natural interfaces for people, e.g., through automatic speech recognition. For instance, Lavrynenko et al [32] propose a radio-based remote control system, in which a semantic identification method based on mel-frequency cepstral (MFC) coefficients is used. The captured audio is translated to an action that, in turn, is transferred to the UAV for its execution.…”

Section: Speech Controlmentioning

confidence: 99%

“…One of the most recent works is an extension of [32] developed by Lavrynenko et al [37]. In this extension, the authors present a similar radio-based control system with cepstral analysis, however, in this work they also add encrypted communication.…”

Section: Speech Controlmentioning

confidence: 99%

Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition

Contreras,

Ayala,

Cruz

2020

Preprint

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Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and reaching out to many areas of society, including the industrialized world. A common alternative to control the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices can be found. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this work, we present a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control is performed using a more natural, human-like way to communicate the instructions. Moreover, we implement 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. The conducted experiments involve participants giving voice commands to the drone in both languages in order to compare the effectiveness of each of them, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion have been applied to the voice commands in order to test the proposed approach when facing noisy input signals.The obtained results show that the unmanned aerial vehicle is capable of interpreting user voice instructions achieving an improvement in speechto-action recognition for both languages when using phoneme matching in comparison to only using the cloud-based algorithm without domainbased instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions respectively, whereas using our phoneme matching approach the results are improved achieving 93.33% and 100.00% accuracy for English and Spanish languages.

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“…In [13], the interoperability of speech recognition in Romanian for voice-controlled appliances in smart buildings was explored. In [14], a voice-based unmanned aerial vehicle approach using the MFCC analysis of semantic identification was introduced. The experiments in previous studies have shown that the MFCC was capable of voice command recognition.…”

Section: Introductionmentioning

confidence: 99%

Thai Voice-Controlled Analysis for Car Parking Assistance in System-on-Chip Architecture

Prongnuch

Sitjongsataporn

2020

Adv. technol. innov.

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This paper introduces an analysis of Thai speech recognition for controlled car parking assist in the system-on-chip architecture. The objective is to investigate the male and female voice command signals, including Thai and English words, issued by the native Thai users. Hardware and software co-design by the Xilinx VIVADO are designed on an ARM multicore processor and a reconfigurable system on a ZYBO board. The experiments for Thai and English word recognition are conducted by using the Mel-frequency cepstral coefficient approach and presented in the form of spectrograms. The comparison of a voice command via Bluetooth and a reference command stored on an SD card and the ZYBO embedded board on a miniature electric vehicle is verified with the Pearson’s correlation coefficient (PCC). The experimental results show the accuracies of the received Thai/English, male/female, and indoor/outdoor voice commands as compared with the reference voice commands in the noisy surroundings. Hence, our system can support Thai/English and male/female voice commands to perform a set of actions for maneuvering a car by the PCC.

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Method of voice control functions of the UAV

Cited by 14 publications

References 7 publications

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition

Thai Voice-Controlled Analysis for Car Parking Assistance in System-on-Chip Architecture

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