  Several sensing techniques have been proposed for silent speech recognition
(SSR); however, many of these methods require invasive processes or sensor
attachment to the skin using adhesive tape or glue, rendering them unsuitable
for frequent use in daily life. By contrast, impulse radio ultra-wideband
(IR-UWB) radar can operate without physical contact with users' articulators
and related body parts, offering several advantages for SSR. These advantages
include high range resolution, high penetrability, low power consumption,
robustness to external light or sound interference, and the ability to be
embedded in space-constrained handheld devices. This study demonstrated IR-UWB
radar-based contactless SSR using four types of speech stimuli (vowels,
consonants, words, and phrases). To achieve this, a novel speech feature
extraction algorithm specifically designed for IR-UWB radar-based SSR is
proposed. Each speech stimulus is recognized by applying a classification
algorithm to the extracted speech features. Two different algorithms,
multidimensional dynamic time warping (MD-DTW) and deep neural network-hidden
Markov model (DNN-HMM), were compared for the classification task.
Additionally, a favorable radar antenna position, either in front of the user's
lips or below the user's chin, was determined to achieve higher recognition
accuracy. Experimental results demonstrated the efficacy of the proposed speech
feature extraction algorithm combined with DNN-HMM for classifying vowels,
consonants, words, and phrases. Notably, this study represents the first
demonstration of phoneme-level SSR using contactless radar.
