CONF Juan_SLT2023_2023/IDIAP BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications Zuluaga-Gomez, Juan Sarfjoo, Seyyed Saeed Prasad, Amrutha Iuliia, Nigmatulina Motlicek, Petr Ondřej, Karel Ohneiser, Oliver air traffic control communications chunking Speaker change detection speaker role detection Text-based speaker diarization EXTERNAL http://publications.idiap.ch/attachments/papers/2022/Juan_SLT2023_2023.pdf PUBLIC IEEE - 2023 IEEE Spoken Language Technology Workshop (SLT) 1 1 1 2023 https://arxiv.org/abs/2110.05781 URL Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech activity detection (SAD) and speaker diarization (SD). In the failure condition, two or more segments remain in the same recording, jeopardizing the overall performance. We propose a system that combines SAD and a BERT model to perform speaker change detection and speaker role detection (SRD) by chunking ASR transcripts, i.e., SD with a defined number of speakers together with SRD. The proposed model is evaluated on real-life public ATC databases. Our BERT SD model baseline reaches up to 10% and 20% token-based Jaccard error rate (JER) in public and private ATC databases. We also achieved relative improvements of 32% and 7.7% in JERs and SD error rate (DER), respectively, compared to VBx, a well-known SD system.