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.
翻译:在本文中, 我们提出一个新的等级, 用于无线频道终端到终端语音传输的语义编码传输方法。 我们将整个系统命名为深语音语义传输( DSST ) 。 具体地说, 我们引入了非线性转换, 将语音源映射成语义潜在空间, 并将语义特征输入源- 通道编码器, 以生成频道输入序列 。 在变式模型理念的指导下, 我们在隐蔽空间上构建了一种昆虫模型模型模型, 以估计语义嵌入器的重要性。 因此, 这些不同重要性的语义特征可以合理地以不同的编码速度分配, 从而最大限度地增加系统编码收益。 此外, 我们引入了一种非线性转换语言源, 将语音源映射到语系潜在空间, 将一个单一模型应用于各个频道状态。 我们模型的端对端对端至端优化导出一个灵活的速调( RD) 123, 支持多功能性无线语音传输。 实验结果验证我们的 DSST 系统在目前设计语音传输模型上明显超越了语音传感器质量系统, 将我们的目标和主观测量成本 。 将SLADRA