Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain

Cristian Tejedor-García, Berrie van der Molen, Henk van den Heuvel, Arjan van Hessen, Toine Pieters


Abstract
The current largest open-source generic automatic speech recognition (ASR) system for Dutch, Kaldi_NL, does not include a domain-specific healthcare jargon in the lexicon. Commercial alternatives (e.g., Google ASR system) are also not suitable for this purpose, not only because of the lexicon issue, but they do not safeguard privacy of sensitive data sufficiently and reliably. These reasons motivate that just a small amount of medical staff employs speech technology in the Netherlands. This paper proposes an innovative ASR training method developed within the Homo Medicinalis (HoMed) project. On the semantic level it specifically targets automatic transcription of doctor-patient consultation recordings with a focus on the use of medicines. In the first stage of HoMed, the Kaldi_NL language model (LM) is fine-tuned with lists of Dutch medical terms and transcriptions of Dutch online healthcare news bulletins. Despite the acoustic challenges and linguistic complexity of the domain, we reduced the word error rate (WER) by 5.2%. The proposed method could be employed for ASR domain adaptation to other domains with sensitive and special category data. These promising results allow us to apply this methodology on highly sensitive audiovisual recordings of patient consultations at the Netherlands Institute for Health Services Research (Nivel).
Anthology ID:
2022.lrec-1.110
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1032–1039
Language:
URL:
https://aclanthology.org/2022.lrec-1.110
DOI:
Bibkey:
Cite (ACL):
Cristian Tejedor-García, Berrie van der Molen, Henk van den Heuvel, Arjan van Hessen, and Toine Pieters. 2022. Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1032–1039, Marseille, France. European Language Resources Association.
Cite (Informal):
Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain (Tejedor-García et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.110.pdf