Peiyuan Jiang1, Weijun Pan1,*, Jian Zhang1, Teng Wang1, Junxiang Huang2
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 911-940, 2023, DOI:10.32604/cmc.2023.041772
- 31 October 2023
Abstract
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
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