Semantic-preserved Communication System for Highly Efficient Speech Transmission

• DOI: 10.48550/arxiv.2205.12727
• Publication Date: 2022-01-01
• Authors: Tianxiao Han; Qianqian Yang; Zhiguo Shi; Shibo He; Zhaoyang Zhang
• Keywords: Speech analytics

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 that focus on abstract symbols, approaches attempt achieve better efficiency by only sending semantic-related information source data. this paper, we consider semantic-oriented which transmits semantic-relevant over channel recognition task, a compact additional set semantic-irrelevant reconstruction task. We propose novel end-to-end DL-based transceiver extracts encodes from input spectrums at transmitter outputs corresponding transcriptions decoded receiver. For transmission, further include CTC alignment module small number but speech-related original signals The simulation results confirm our proposed method outperforms current terms accuracy predicted text quality recovered significantly improves efficiency. More specifically, sends 16% amount transmitted symbols required existing while achieving about 10% reduction WER transmission. it an even more remarkable improvement with 0.2% method.