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Through-Wall Remote Human Voice Recognition Using Doppler Radar With Transfer Learning
- Through-Wall Remote Human Voice Recognition Using Doppler Radar With Transfer Learning
- Khanna, Rohan; Oh, Daegun; Kim, Youngwook
- DGIST Authors
- Oh, Daegun
- Issue Date
- IEEE Sensors Journal, 19(12), 4571-4576
- Article Type
- Author Keyword
- Human voice recognition; micro-Doppler signatures; deep learning; convolutional neural network; transfer learning; AlexNet; VGG-16
- Convolution; Deep learning; Deep neural networks; Doppler radar; Neural networks; AlexNet; Convolutional neural network; Human voice recognition; Micro-Doppler; Transfer learning; VGG-16; Speech recognition
- We investigated the feasibility of using Doppler radar to recognize human voices by capturing the micro-Doppler signatures of vibrations from the larynx and mouth. The signatures produced through the vibrations of a human being's vocal cords generate unique micro-Doppler signatures, depending on the letters pronounced. These can then be used to classify and recognize different words and letters. In this paper, we could successfully capture echo signals using the Doppler radar when a human subject spoke seven musical notes from Do to Ti and alphabet letters from A to Z. Spectrogram analysis was conducted for classification purposes, and the deep convolutional neural networks employed could classify the 26 letters to an accuracy of 94%. To overcome the deficiency of the measured data and improve the classification accuracy, transfer learning was introduced. Using the VGG-16 model, its accuracy was improved up to 97%. Additional experiments were conducted to ascertain the radar's capability to detect the human voice through a barrier between the human and the radar. In this paper, we demonstrated the possibility of remote voice recognition using Doppler information, with or without a barrier. © 2019 IEEE.
- Institute of Electrical and Electronics Engineers
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