A Deep Learning Approach to Speech Based Control of Unmanned Aerial Vehicles (UAVs)

Kumaar, Saumya; Bazaz, Toshit; Kour, Sarabdeep; Gupta, Deepali; Vishwanath, R.; Omkar, S. N.

doi:10.5121/csit.2018.81003

Cited by 1 publication

(1 citation statement)

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“…According to the classification report, the model achieved a quantitative evaluation with an average of 87% for precision and 83% for recall. An alternate approach to speech recognition for robotics applications based on a combination of spectrograms, MEL and MFCC features, and a deep neural network-based classification is presented in [26]. The algorithm's overall validation accuracy is as high as 97%, whereas the testing accuracy of the system is 95.4%.…”

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%