Discourse Feature Recognition for Text Dynamic Translation

Meng Li

2022

Abstract

In order to improve the ability of dynamic translation of foreign media discourse, this paper puts forward a method of discourse feature recognition and recognition based on spectral density feature decomposition. Using time-frequency dynamic feature analysis, the dynamic compensation model of foreign media text translation discourse signal is established, and the noise parameters of foreign media text translation discourse signal are analyzed and recognized by combining the frequency spectrum density feature estimation method. The feature decomposition model of foreign media text translation discourse signal is constructed by multi-frame speech compensation and parameter modulation method, and the wavelet multi-level feature detection method is adopted. The anti-interference filtering analysis of foreign media text translation discourse is realized. The conditional coding of text translation discourse signal is carried out by the method of spectral density dynamic feature decomposition, and the statistical information analysis model of foreign media text translation discourse signal is established. The signal detection and feature recognition of foreign media text translation discourse signal are realized by text parameter clustering and envelope amplitude-frequency feature detection. The test results show that the accuracy of foreign media text translation discourse feature recognition by this method is high, and the output signal-to-noise ratio of the signal is improved by discourse feature recognition.

Paper Citation

in Harvard Style

Li M. (2022). Discourse Feature Recognition for Text Dynamic Translation. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 578-583. DOI: 10.5220/0011752300003607

in Bibtex Style

@conference{icpdi22,
author={Meng Li},
title={Discourse Feature Recognition for Text Dynamic Translation},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={578-583},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011752300003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - Discourse Feature Recognition for Text Dynamic Translation
SN - 978-989-758-620-0
AU - Li M.
PY - 2022
SP - 578
EP - 583
DO - 10.5220/0011752300003607
PB - SciTePress