  Deep learning (DL) based semantic communication methods have been explored
for the efficient transmission of images, text, and speech in recent years. In
contrast to traditional wireless communication methods that focus on the
transmission of abstract symbols, semantic communication approaches attempt to
achieve better transmission efficiency by only sending the semantic-related
information of the source data. In this paper, we consider semantic-oriented
speech transmission which transmits only the semantic-relevant information over
the channel for the speech recognition task, and a compact additional set of
semantic-irrelevant information for the speech reconstruction task. We propose
a novel end-to-end DL-based transceiver which extracts and encodes the semantic
information from the input speech spectrums at the transmitter and outputs the
corresponding transcriptions from the decoded semantic information at the
receiver. For the speech to speech transmission, we further include a CTC
alignment module that extracts a small number of additional semantic-irrelevant
but speech-related information for the better reconstruction of the original
speech signals at the receiver. The simulation results confirm that our
proposed method outperforms current methods in terms of the accuracy of the
predicted text for the speech to text transmission and the quality of the
recovered speech signals for the speech to speech transmission, and
significantly improves transmission efficiency. More specifically, the proposed
method only sends 16% of the amount of the transmitted symbols required by the
existing methods while achieving about 10% reduction in WER for the speech to
text transmission. For the speech to speech transmission, it results in an even
more remarkable improvement in terms of transmission efficiency with only 0.2%
of the amount of the transmitted symbols required by the existing method.
