1 research outputs found
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment
Automatic Speech Recognition (ASR) is greatly developed in recent years,
which expedites many applications on other fields. For the ASR research, speech
corpus is always an essential foundation, especially for the vertical industry,
such as Air Traffic Control (ATC). There are some speech corpora for common
applications, public or paid. However, for the ATC, it is difficult to collect
raw speeches from real systems due to safety issues. More importantly, for a
supervised learning task like ASR, annotating the transcription is a more
laborious work, which hugely restricts the prospect of ASR application. In this
paper, a multilingual speech corpus (ATCSpeech) from real ATC systems,
including accented Mandarin Chinese and English, is built and released to
encourage the non-commercial ASR research in ATC domain. The corpus is detailly
introduced from the perspective of data amount, speaker gender and role, speech
quality and other attributions. In addition, the performance of our baseline
ASR models is also reported. A community edition for our speech database can be
applied and used under a special contrast. To our best knowledge, this is the
first work that aims at building a real and multilingual ASR corpus for the air
traffic related research