This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents ...This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.展开更多
By employing the improved T42L5 spectral model and utilizing the ECMWF data covering the period from 1 July to 7 July 1982,a numerical research on the formation of the Ural blocking system has been made.The results sh...By employing the improved T42L5 spectral model and utilizing the ECMWF data covering the period from 1 July to 7 July 1982,a numerical research on the formation of the Ural blocking system has been made.The results show that the model forecasts for the upstream U ral area turn out to be worse if the dynamic effect of the Qinghai-Xizang Plateau is not considered.The correlation coefficient between the model forecasts and observed 500 hPa geopotential height anomaly decreases by 9% for the 5-day mean,and their averaged root mean square (RMS) error increases 15 m.Due to the dynamic effect of the Plateau,the trough being on the northwest of the Plateau is barricaded and turns to be a transversal trough.Consequently southwest flow occurs along the northwest of the Plateau in front of the trough,while northeast flow prevails over the west of the trough,causing the formation of the blocking high over the Ural area.When the dynamic effect of the Plateau is not taken into consideration,the trough develops and moves southeastward and the Ural blocking high changes into a migratory high.All these result in the failure of the simulation.The dynamic effect of the Plateau helps to increase the negative vorticities over the Plateau and its north periphery as well as the Ural area,and also helps to increase the positive vorticities over the Black Sea and the Caspian Sea area.On the other hand,the thermodynamic effect mainly influences the Plateau and its downstream area and plays an less important role in the formation of the blocking high over the upstream Ural area.展开更多
基金This study was co-supported by the National Key R&D Program of China(No.2021YFF0603904)National Natural Science Foundation of China(U1733203)Safety Capacity Building Project of Civil Aviation Administration of China(TM2019-16-1/3).
文摘This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.
文摘By employing the improved T42L5 spectral model and utilizing the ECMWF data covering the period from 1 July to 7 July 1982,a numerical research on the formation of the Ural blocking system has been made.The results show that the model forecasts for the upstream U ral area turn out to be worse if the dynamic effect of the Qinghai-Xizang Plateau is not considered.The correlation coefficient between the model forecasts and observed 500 hPa geopotential height anomaly decreases by 9% for the 5-day mean,and their averaged root mean square (RMS) error increases 15 m.Due to the dynamic effect of the Plateau,the trough being on the northwest of the Plateau is barricaded and turns to be a transversal trough.Consequently southwest flow occurs along the northwest of the Plateau in front of the trough,while northeast flow prevails over the west of the trough,causing the formation of the blocking high over the Ural area.When the dynamic effect of the Plateau is not taken into consideration,the trough develops and moves southeastward and the Ural blocking high changes into a migratory high.All these result in the failure of the simulation.The dynamic effect of the Plateau helps to increase the negative vorticities over the Plateau and its north periphery as well as the Ural area,and also helps to increase the positive vorticities over the Black Sea and the Caspian Sea area.On the other hand,the thermodynamic effect mainly influences the Plateau and its downstream area and plays an less important role in the formation of the blocking high over the upstream Ural area.
