Real-time translation of English speech through speech feature extraction

Real-time English speech translation is useful in numerous situations, including business and travel. The goal of this research is to improve real-time English speech translation efficacy. Initially, filter bank (FBank) features were extracted from English speech. Subsequently, an enhanced Transformer model was introduced, incorporating a causal convolution module in the front end of the encoder to capture English speech features with location information. The performance of the optimized model in translating English speech to different target languages was tested using the MuST-C dataset. The results revealed differences in translation results for different target languages using the improved Transformer. The highest bilingual evaluation understudy (BLEU) score was observed for Spanish text at 20.84, while Russian text obtained the lowest score of 10.56. The average BLEU score was 18.51, with an average lag time delay of 1202.33 ms. Compared to the conventional Transformer model, the improved model exhibited higher BLEU scores, lower time delay, and optimal performance when utilizing a convolutional kernel size of 3 x 3. The results demonstrate the dependability of the improved Transformer model in real-time English speech translation, highlighting its practical usefulness.