Error Correction Method of Business English Translation Based on Convolutional Neural Network

Abstract

In order to correct business English translation errors, this paper puts forward a method of business English translation error correction based on convolutional neural network and English pronunciation feature recognition. The blind convolution network spectrum parameter detection method is used to detect the pronunciation spectrum features of business English translation, and the scalar time series of pronunciation output audio parameter sequence and translated text semantic feature sequence are established. Combined with the noise intensity detection and signal scale decomposition method of business English translation pronunciation audio time series, the detailed signal energy parameters of business English translation pronunciation audio time series are extracted, and the convolution neural network classification method is used to classify the features. The interference component of single audio feature sequence of English translation pronunciation is removed by high-frequency wavelet threshold detection, and the modulation and demodulation of single audio feature sequence of English translation pronunciation are realized by using translation dictionary set and semantic context matching. The spectral analysis and error correction model of business English translation pronunciation audio time series is established, and the output stability of business English translation pronunciation audio time series is detected by threshold detection on each scale. According to the difference between output signal and pronunciation standard signal, the accuracy of English translator is detected and identified. The simulation results show that the accuracy of business English translation error correction with this method is high, the detection performance is good, and the output accuracy of English translators is improved.