Semantic-aware Speech to Text Transmission with Redundancy Removal
This work addresses efficient speech-to-text transmission for wireless communication systems, offering an incremental improvement over existing semantic communication methods.
The paper tackles semantic-aware speech-to-text transmission by proposing an end-to-end deep learning transceiver with attention-based soft alignment and redundancy removal modules, achieving higher accuracy and transmission efficiency than current methods while reducing model size and runtime.
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.