  In this paper, we propose a new class of high-efficiency semantic coded
transmission methods for end-to-end speech transmission over wireless channels.
We name the whole system as deep speech semantic transmission (DSST).
Specifically, we introduce a nonlinear transform to map the speech source to
semantic latent space and feed semantic features into source-channel encoder to
generate the channel-input sequence. Guided by the variational modeling idea,
we build an entropy model on the latent space to estimate the importance
diversity among semantic feature embeddings. Accordingly, these semantic
features of different importance can be allocated with different coding rates
reasonably, which maximizes the system coding gain. Furthermore, we introduce a
channel signal-to-noise ratio (SNR) adaptation mechanism such that a single
model can be applied over various channel states. The end-to-end optimization
of our model leads to a flexible rate-distortion (RD) trade-off, supporting
versatile wireless speech semantic transmission. Experimental results verify
that our DSST system clearly outperforms current engineered speech transmission
systems on both objective and subjective metrics. Compared with existing neural
speech semantic transmission methods, our model saves up to 75% of channel
bandwidth costs when achieving the same quality. An intuitive comparison of
audio demos can be found at https://ximoo123.github.io/DSST.
