  In this paper, we develop a deep learning based semantic communication system
for speech transmission, named DeepSC-ST. We take the speech recognition and
speech synthesis as the transmission tasks of the communication system,
respectively. First, the speech recognition-related semantic features are
extracted for transmission by a joint semantic-channel encoder and the text is
recovered at the receiver based on the received semantic features, which
significantly reduces the required amount of data transmission without
performance degradation. Then, we perform speech synthesis at the receiver,
which dedicates to re-generate the speech signals by feeding the recognized
text and the speaker information into a neural network module. To enable the
DeepSC-ST adaptive to dynamic channel environments, we identify a robust model
to cope with different channel conditions. According to the simulation results,
the proposed DeepSC-ST significantly outperforms conventional communication
systems and existing DL-enabled communication systems, especially in the low
signal-to-noise ratio (SNR) regime. A software demonstration is further
developed as a proof-of-concept of the DeepSC-ST.
