  The traditional communications transmit all the source data represented by
bits, regardless of the content of source and the semantic information required
by the receiver. However, in some applications, the receiver only needs part of
the source data that represents critical semantic information, which prompts to
transmit the application-related information, especially when bandwidth
resources are limited. In this paper, we consider a semantic communication
system for speech recognition by designing the transceiver as an end-to-end
(E2E) system. Particularly, a deep learning (DL)-enabled semantic communication
system, named DeepSC-SR, is developed to learn and extract text-related
semantic features at the transmitter, which motivates the system to transmit
much less than the source speech data without performance degradation.
Moreover, in order to facilitate the proposed DeepSC-SR for dynamic channel
environments, we investigate a robust model to cope with various channel
environments without requiring retraining. The simulation results demonstrate
that our proposed DeepSC-SR outperforms the traditional communication systems
in terms of the speech recognition metrics, such as character-error-rate and
word-error-rate, and is more robust to channel variations, especially in the
low signal-to-noise (SNR) regime.
