Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition
Idiap Research Institute（Idiap研究所）；Brno University of Technology（布尔诺理工大学）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Zurich（苏黎世大学）；German Aerospace Center (DLR)（德国航空航天中心）
Amrutha Prasad, Juan Zuluaga-Gomez, Petr Motlicek, Saeed Sarfjoo, Iuliia Nigmatulina, Oliver Ohneiser, Hartmut Helmke
Automatic Speech Recognition (ASR) for air traffic control is generally trained by pooling Air Traffic Controller (ATCO) and pilot data into one set. This is motivated by the fact that pilot's voice communications are more scarce than ATCOs. Due to this data imbalance and other reasons (e.g., varying acoustic conditions), the speech from ATCOs is usually recognized more accurately than from pilots. Automatically identifying the speaker roles is a challenging task, especially in the case of the noisy voice recordings collected using Very High Frequency (VHF) receivers or due to the unavailability of the push-to-talk (PTT) signal, i.e., both audio channels are mixed. In this work, we propose to (1) automatically segment the ATCO and pilot data based on an intuitive approach exploiting ASR transcripts and (2) subsequently consider an automatic recognition of ATCOs' and pilots' voice as two separate tasks. Our work is performed on VHF audio data with high noise levels, i.e., signal-to-noise (SNR) ratios below 15 dB, as this data is recognized to be helpful for various speech-based machine-learning tasks. Specifically, for the speaker role identification task, the module is represented by a simple yet efficient knowledge-based system exploiting a grammar defined by the International Civil Aviation Organization (ICAO). The system accepts text as the input, either manually verified annotations or automatically generated transcripts. The developed approach provides an average accuracy in speaker role identification of about 83%. Finally, we show that training an acoustic model for ASR tasks separately (i.e., separate models for ATCOs and pilots) or using a multitask approach is well suited for the noisy data and outperforms the traditional ASR system where all data is pooled together.
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications
Idiap Research Institute（伊迪亚普研究所）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Brno University of Technology（布尔诺理工大学）；German Aerospace Center (DLR)（德国航空航天中心）
Automatic Speech Recognition Benchmark for Air-Traffic Communications
Idiap Research Institute（Idiap 研究所）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Beijing Institute of Technology（北京理工大学）；Brno University of Technology（布尔诺理工大学）
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems
Idiap Research Institute（伊迪亚普研究所）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Brno University of Technology（布尔诺理工大学）；ReplayWell（ReplayWell公司）
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment
National Key Laboratory of Air Traffic Control Automation System Technology, Sichuan University（四川大学）；Southwest Air Traffic Management Bureau, Civil Aviation Administration of China (CAAC)（中国民用航空局西南空中交通管理局）；Wisesoft Co., Ltd.（四川川大智胜软件股份有限公司）
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications
Idiap Research Institute（Idiap 研究所）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Brno University of Technology（布尔诺理工大学）；ReplayWell（ReplayWell公司）；Saarland University（萨尔大学）；Evaluations and Language Resources Distribution Agency (ELDA)（评估与语言资源分发局）；Romagna Tech（罗马涅科技公司）；Honeywell（霍尼韦尔）；OpenSky Network（开放天空网络）
