# Speech Detection For Child-Clinician Conversations In Danish For Low-Resource In-The-Wild Conditions: A Case Study

arXiv: https://arxiv.org/abs/2204.11550 · HTML: https://mlsanity.com/paper/2204.11550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Speech recognition for medical conversations](https://mlsanity.com/paper/1711.07274/) (2017)
- [An open-source voice type classifier for child-centered daylong recordings](https://mlsanity.com/paper/2005.12656/) (2020)
- [Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction](https://mlsanity.com/paper/2104.02219/) (2021)
- [Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation](https://mlsanity.com/paper/2010.15090/) (2020)

## Built on by (most cited first, 1 of 1)

- [EmoTale: An Enacted Speech-emotion Dataset in Danish](https://mlsanity.com/paper/2508.14548/) (2025)

## Abstract (arXiv)

Use of speech models for automatic speech processing tasks can improve efficiency in the screening, analysis, diagnosis and treatment in medicine and psychiatry. However, the performance of pre-processing speech tasks like segmentation and diarization can drop considerably on in-the-wild clinical data, specifically when the target dataset comprises of atypical speech. In this paper we study the performance of a pre-trained speech model on a dataset comprising of child-clinician conversations in Danish with respect to the classification threshold. Since we do not have access to sufficient labelled data, we propose few-instance threshold adaptation, wherein we employ the first minutes of the speech conversation to obtain the optimum classification threshold. Through our work in this paper, we learned that the model with default classification threshold performs worse on children from the patient group. Furthermore, the error rates of the model is directly correlated to the severity of diagnosis in the patients. Lastly, our study on few-instance adaptation shows that three-minutes of clinician-child conversation is sufficient to obtain the optimum classification threshold.