Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech to text transmission. We propose a novel end-to-end DL-based transceiver, which includes an attention-based soft alignment module and a redundancy removal module to compress the transmitted data. In particular, the former extracts only the text-related semantic features, and the latter further drops the semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a two-stage training scheme, which speeds up the training of the proposed DL model. The simulation results indicate that our proposed method outperforms current methods in terms of the accuracy of the received text and transmission efficiency. Moreover, the proposed method also has a smaller model size and shorter end-to-end runtime.
翻译:近年来,为有效传输图像、文本和语音,探索了基于语义的深层学习(DL)基础语义通信方法。与传统的侧重于传输抽象符号的无线通信方法不同,语义通信方法试图通过仅发送源数据中语义相关信息来提高传输效率。在本文件中,我们考虑以语义为主的演讲为文本传输。我们提议了一个基于语义的新式端到端的DL收发器,其中包括一个基于关注的软对齐模块和一个冗余清除模块,以压缩传输的数据。特别是,前者只提取与文字有关的语义特征,而后者进一步删除了语义冗余内容,与现有方法相比,大大减少了语义冗余内容的数量。我们还提议了一个两阶段培训计划,以加快对拟议DL模式的培训。模拟结果表明,我们拟议的方法在接收文本的准确性和传输效率方面超越了当前方法。此外,拟议方法的模型规模较小,端至端运行时间较短。