Transfer Learning for Air Traffic Control LVCSR System
Jiawen Wang, Shaohan Liu, Qun Yang · 2017
In order to reduce the accidents due to errors of Air Traffic Control (ATC) directives and do responsibility investigation, it's necessary to recognize the audio of the ATC directives into texts. However, the existing Automatic Speech Recognition (ASR) systems are aimed for isolated word recognition, which can't apply to LVCSR. Thus, we analyze the characteristics of ATC directives and develop Large Vocabulary Continuous Speech Recognition (LVCSR) for it. In addition, to solve the issue, that the data of ATC directives are scarce, we proposed a new crosslingual knowledge transfer learning method, i.e. semi-shared-hidden layers crosslingual (Semi-SHL-CDNN). We demonstrate that the Semi-SHL-CDNN can reduce errors by 16.76%, relatively, over monolingual DNNs. Compared with SHL-MDNN, the WER is reduced by 1.38% extra.