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 [BibTeX] [Marc21]
Grammar Based Identification Of Speaker Role For Improving ATCO And Pilot ASR
Type of publication: Idiap-RR
Citation: Prasad_Idiap-RR-22-2021
Number: Idiap-RR-22-2021
Year: 2021
Month: 12
Institution: Idiap
Abstract: Assistant Based Speech Recognition (ABSR) for air traffic control is generally trained by pooling both Air Traffic Controller (ATCO) and pilot data. In practice, this is motivated by the fact that the proportion of pilot data is lesser compared to ATCO while their standard language of communication is similar. However, due to data imbalance of ATCO and pilot and their varying acoustic conditions, the ASR performance is usually significantly better for ATCOs than pilots. In this paper, we propose to (1) split the ATCO and pilot data using an automatic approach exploiting ASR transcripts, and (2) consider ATCO and pilot ASR as two separate tasks for Acoustic Model (AM) training. For speaker role classification of ATCO and pilot data, a hypothesized ASR transcript is generated with a seed model, subsequently used to classify the speaker role based on the knowledge extracted from grammar defined by International Civil Aviation Organization (ICAO). This approach provides an average speaker role identification accuracy of 83% for ATCO and pilot. Finally, we show that training AMs separately for each task, or using a multitask approach is well suited for this data compared to AM trained by pooling all data.
Projects Idiap
EC H2020- ATCO2
Authors Prasad, Amrutha
Juan, Zuluaga-Gomez.
Motlicek, Petr
Ohneiser, Oliver
Helmke, Hartmut
Sarfjoo, Seyyed Saeed
Iuliia, Nigmatulina
Added by: [ADM]
Total mark: 0
  • Prasad_Idiap-RR-22-2021.pdf