Wireless Deep Speech Semantic Transmission

TL;DR

This paper introduces a novel deep speech semantic transmission system that efficiently encodes speech for wireless channels, significantly reducing bandwidth costs while maintaining high quality through semantic importance-aware coding and SNR adaptation.

Contribution

The paper presents a new end-to-end neural framework for wireless speech transmission that incorporates semantic importance modeling and SNR adaptation, outperforming existing methods.

Findings

Outperforms current systems on objective and subjective metrics.

Reduces channel bandwidth costs by up to 75%.

Supports flexible rate-distortion trade-offs.

Abstract

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.