End-to-End Chinese-English Speech Recognition Based on Conformer in Air Traffic Control
Applying speech recognition technology to air traffic management systems can enhance flight safety and reduce the workload of air traffic controllers.Currently,existing speech recognition technologies for air traffic control perform poorly in recognizing both Chinese and English.Therefore,a Conformer-CTC/Attention-based framework for Chinese-English air traffic control speech recognition is proposed.This method utilizes aimproved Conformer-based shared encoder for language classification of input se-quences and effectively models local and global dependencies in audio sequences.It incorporates a lan-guage classification module to determine the language of the input speech sequence.It also employs a multi-task modeling approach with both CTC and attention decoders for joint decoding.Finally,the pro-posed framework is validated on a constructed civil aviation dataset.Experimental results indicate that Conformer-CTC/Attention(Language-Category)achieves a lower error rate compared to the baseline model,demonstrating the expected recognition performance improvement.
air traffic controlChinese-English Speech RecognitionConformer-CTC/Attentionmulti-task learningend-to-end
万方数据
维普
