论文标题
躁郁症自动躁狂评估的语音分析
Speech Analysis for Automatic Mania Assessment in Bipolar Disorder
论文作者
论文摘要
躁郁症是一种精神疾病，会导致躁狂和抑郁发作时期。在这项工作中，我们将包含7个不同任务的双相情感障碍语料库的记录分类为仅使用语音特征的躁狂症，躁狂和缓解类别。我们对访谈中分裂的任务进行实验。在接受第六和第七任务训练的模型上取得的最佳结果可为0.53 UAR（未加权平均召回）结果，该结果高于语料库的基线结果。
Bipolar disorder is a mental disorder that causes periods of manic and depressive episodes. In this work, we classify recordings from Bipolar Disorder corpus that contain 7 different tasks, into hypomania, mania, and remission classes using only speech features. We perform our experiments on splitted tasks from the interviews. Best results achieved on the model trained with 6th and 7th tasks together gives 0.53 UAR (unweighted average recall) result which is higher than the baseline results of the corpus.
