Speech-based natural language interface for UAV trajectory generation

Meszaros, Erica L.; Chandarana, Meghan; Trujillo, Anna C.; Allen, B. Danette

doi:10.1109/icuas.2017.7991401

Cited by 5 publications

(4 citation statements)

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“…A speaker installed on the UAV causes victims to react, and their voices are recorded to detect them. In [24], the authors investigate a speech-based natural language interface for defining UAV trajectories. To determine the effectiveness of this interface, a user study comparing its performance to that of a conventional mouse-based interface is also presented.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Lingual Speech Recognition-Based Framework to Human-Drone Interaction

et al. 2022

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In recent years, human–drone interaction has received increasing interest from the scientific community. When interacting with a drone, humans assume a variety of roles, the nature of which are determined by the drone’s application and degree of autonomy. Common methods of controlling drone movements include by RF remote control and ground control station. These devices are often difficult to manipulate and may even require some training. An alternative is to use innovative methods called natural user interfaces that allow users to interact with drones in an intuitive manner using speech. However, using only one language of interacting may limit the number of users, especially if different languages are spoken in the same region. Moreover, environmental and propellers noise make speech recognition a complicated task. The goal of this work is to use a multilingual speech recognition system that includes English, Arabic, and Amazigh to control the movement of drones. The reason for selecting these languages is that they are widely spoken in many regions, particularly in the Middle East and North Africa (MENA) zone. To achieve this goal, a two-stage approach is proposed. During the first stage, a deep learning based model for multilingual speech recognition is designed. Then, the developed model is deployed in real settings using a quadrotor UAV. The network was trained using 38,850 records including commands and unknown words mixed with noise to improve robustness. An average class accuracy of more than 93% has been achieved. After that, experiments were conducted involving 16 participants giving voice commands in order to test the efficiency of the designed system. The achieved accuracy is about 93.76% for English recognition and 88.55%, 82.31% for Arabic and Amazigh, respectively. Finally, hardware implementation of the designed system on a quadrotor UAV was made. Real time tests have shown that the approach is very promising as an alternative form of human–drone interaction while offering the benefit of control simplicity.

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Section: Related Workmentioning

confidence: 99%

A Multi-Lingual Speech Recognition-Based Framework to Human-Drone Interaction

et al. 2022

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“…[3] Additionally, a major strength of designing a multi-modal NUI is that the user can utilize any of the natural interactions interchangeably depending on their unique needs and those of the mission. [2] Recently, speech-based natural language interfaces have become commonplace [1,2,3,4] as they allow users a natural, intuitive way to communicate with machines, whether at home, work, or in between. [4] Using a speech-based natural language interface is both convenient and efficient and has proven to increase situational awareness, usability, and satisfaction.…”

Section: Introductionmentioning

confidence: 99%

“…[2] Recently, speech-based natural language interfaces have become commonplace [1,2,3,4] as they allow users a natural, intuitive way to communicate with machines, whether at home, work, or in between. [4] Using a speech-based natural language interface is both convenient and efficient and has proven to increase situational awareness, usability, and satisfaction. They allow humans to interact with machine teammates as they would with human teammates.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, a multimodal approach using a combination of input/output modalities (speech/audio, typing/text, touch, and gesture) should be considered to facilitate easier and more intuitive communication between humans and machines. [4] Thus, creating a NUI that is multimodal, efficient, reliable and intuitive is the basis of the ATTRACTOR HMI team's research. This interface will be used to define the number of assets and their roles, the area of interest, the target of the mission, as well as enable interactions during the mission that will aid in the trust and trustworthiness that ATTRACTOR is researching in the hopes it can pave a path to certification.…”

Section: Introductionmentioning

confidence: 99%

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