论文标题
使用监视信息对嘈杂的空中交易记录的呼叫签名识别和理解
Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information
论文作者
论文摘要
空中交通管制（ATC）依赖于飞行员和空运控制器（ATCO）之间的语音进行通信。作为每次飞行的唯一标识符，呼叫符号用于解决ATCO的特定飞行员。从通信中提取呼叫 - 符号是一个挑战，因为ATC语音通道和接收器引入的额外噪声。语音中的低信噪比（SNR）导致高单词错误率（WER）转录本。我们提出了一个新的呼叫识别和理解（CRU）系统，以解决此问题。对识别器进行了培训，可以识别嘈杂的ATC成绩单中的呼叫签名，并将其转换为标准的国际民航组织（ICAO）格式。通过合并监视信息，我们可以将呼叫符号准确性（CSA）乘以四倍。引入的数据增强功能在高WER成绩单上增加了额外的性能，并允许模型适应未见空间。
Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and the additional noise introduced by the receiver. A low signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER) transcripts. We propose a new call-sign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify call-signs in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format. By incorporating surveillance information, we can multiply the call-sign accuracy (CSA) up to a factor of four. The introduced data augmentation adds additional performance on high WER transcripts and allows the adaptation of the model to unseen airspaces.
