  Ensuring intelligible speech communication for hearing assistive devices in
low-latency scenarios presents significant challenges in terms of speech
enhancement, coding and transmission. In this paper, we propose novel solutions
for low-latency joint speech transmission and enhancement, leveraging deep
neural networks (DNNs). Our approach integrates two state-of-the-art DNN
architectures for low-latency speech enhancement and low-latency analog joint
source-channel-based transmission, creating a combined low-latency system and
jointly training both systems in an end-to-end approach. Due to the
computational demands of the enhancement system, this order is suitable when
high computational power is unavailable in the decoder, like hearing assistive
devices. The proposed system enables the configuration of total latency,
achieving high performance even at latencies as low as 3 ms, which is typically
challenging to attain. The simulation results provide compelling evidence that
a joint enhancement and transmission system is superior to a simple
concatenation system in diverse settings, encompassing various wireless channel
conditions, latencies, and background noise scenarios.
