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.

本文研究在野外临床数据中使用预训练语音模型进行分割和分辨，提出了少实例阈值适应方法，并发现默认分类阈值下的模型在患者群体中表现较差，错误率与患者病情的严重程度直接相关，而研究表明三分钟的临床医生 - 儿童对话足以获得最佳分类阈值。