@inproceedings{huang-etal-2024-conec,
    title = "{C}on{EC}: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition",
    author = "Huang, Ruizhe  and
      Yarmohammadi, Mahsa  and
      Trmal, Jan  and
      Liu, Jing  and
      Raj, Desh  and
      Garcia, Leibny Paola  and
      Ivanov, Alexei V.  and
      Ehlen, Patrick  and
      Yu, Mingzhi  and
      Povey, Dan  and
      Khudanpur, Sanjeev",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.328",
    pages = "3700--3706",
    abstract = "Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {---} such as the speaker{'}s contacts or recent song playlists {---} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({``}ConEC{''}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{'} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology",
}

Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {---} such as the speaker{'}s contacts or recent song playlists {---} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({``}ConEC{''}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{'} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology
Huang, Ruizhe Yarmohammadi, Mahsa Trmal, Jan Liu, Jing Raj, Desh Garcia, Leibny Paola Ivanov, Alexei V. Ehlen, Patrick Yu, Mingzhi Povey, Dan Khudanpur, Sanjeev
ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition