计算机科学
言语翻译
机器翻译
语音识别
稳健性(进化)
任务(项目管理)
自然语言处理
人工智能
语义学(计算机科学)
语音活动检测
语音处理
人工神经网络
传输(电信)
电信
程序设计语言
基因
经济
化学
管理
生物化学
作者
Zhenzi Weng,Zhijin Qin,Xiaoming Tao
标识
DOI:10.1109/vtc2023-fall60731.2023.10333632
摘要
Semantic communications execute intelligent tasks at the receiver by only transmitting necessary information. In this paper, we introduce TOS-ST, a task-oriented semantic communication system for speech transmission, which efficiently serves the semantic tasks at the receiver, including speech-to-text translation and speech-to-speech translation. Particularly, TOS-ST condenses the input speech in the source language and extracts the task-related semantics features prior to transmission. At the receiver, these features are recovered and utilized by the neural network-based semantic preserver and machine translation module to generate the uncorrupted text in the target language. To perform the speech-to-speech translation task, the translated text passes through a sophisticated neural network to obtain speech in the target language. According to the simulation results, the TOS-ST outperforms conventional speech transmission systems and exhibits higher robustness against channel impairment.
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