  Speech is one of the most common forms of communication in humans. Speech
commands are essential parts of multimodal controlling of prosthetic hands. In
the past decades, researchers used automatic speech recognition systems for
controlling prosthetic hands by using speech commands. Automatic speech
recognition systems learn how to map human speech to text. Then, they used
natural language processing or a look-up table to map the estimated text to a
trajectory. However, the performance of conventional speech-controlled
prosthetic hands is still unsatisfactory. Recent advancements in
general-purpose graphics processing units (GPGPUs) enable intelligent devices
to run deep neural networks in real-time. Thus, architectures of intelligent
systems have rapidly transformed from the paradigm of composite subsystems
optimization to the paradigm of end-to-end optimization. In this paper, we
propose an end-to-end convolutional neural network (CNN) that maps speech 2D
features directly to trajectories for prosthetic hands. The proposed
convolutional neural network is lightweight, and thus it runs in real-time in
an embedded GPGPU. The proposed method can use any type of speech 2D feature
that has local correlations in each dimension such as spectrogram, MFCC, or
PNCC. We omit the speech to text step in controlling the prosthetic hand in
this paper. The network is written in Python with Keras library that has a
TensorFlow backend. We optimized the CNN for NVIDIA Jetson TX2 developer kit.
Our experiment on this CNN demonstrates a root-mean-square error of 0.119 and
20ms running time to produce trajectory outputs corresponding to the voice
input data. To achieve a lower error in real-time, we can optimize a similar
CNN for a more powerful embedded GPGPU such as NVIDIA AGX Xavier.
