由于抑郁症的检测方式主观性较强,因此使用用户语音诊断抑郁症已成为一种较具有潜力的辅助方式,但不同用户的语音信号存在差异.文中提出了一个跨用户语音域适应网络(Cross User Audio Domain Adaptation Network,CUADAN)来检测抑郁症.从语音中提取可视化的梅尔频谱,利用CUADAN模型的特征提取器从梅尔频谱中获取更深层次的抑郁特征.由于源域和目标域中包含不同健康用户和抑郁用户的语音特征,因此利用CUADAN模型的域分类器在不同用户数据之间进行域适应,从而通过已有分类器对未知用户进行检测.实验结果表明,CUADAN模型的抑郁症检测准确率更高,其平均准确率达到81.0±2.4%.因此,CUADAN模型可以有效削弱不同用户语音之间的差异性,提高跨用户抑郁症检测的准确率.
Depression Detection Based on Cross User Audio Domain Adaptation Network
Because of the subjective detection of depression,the use of user voice diagnosis of depression has become a more potential auxiliary way.However,the speech signals of different users are different.In this study,a CUADAN(Cross User Audio Domain Adaptation Network)is proposed to detect depression.Visual Mel spectrograms are extracted from the audio,and the feature extractor of the CUADAN model is used to extract deeper depression fea-tures from the Mel spectrograms.Since the source domain and target domain contain the voice features of different healthy users and depressed users,the domain classifier of CUADAN model is used to perform domain adaptation be-tween different user data,so that unknown users can be detected by existing classifiers.The experimental results show that the CUADAN model has a higher depression detection accuracy,with an average accuracy of 81.0±2.4%.Therefore,the CUADAN model can effectively weaken the differences between different users'voices and im-prove the accuracy of cross-user depression detection.
万方数据
维普
