论文标题
自动抑郁检测：情感音频文本语料库和基于GRU/BILSTM的模型
Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model
论文作者
论文摘要
抑郁症是全球心理健康问题，最糟糕的情况可能导致自杀。自动抑郁检测系统为促进抑郁症的自我评估和提高诊断准确性提供了极大的帮助。在这项工作中，我们提出了一种新型的抑郁症检测方法，利用参与者的访谈中的语音特征和语言内容。此外，我们还建立了一个情感的音频文本抑郁症语料库（EATD-Corpus），其中包含音频和提取的转录本，以抑郁和不抑郁的志愿者的反应。据我们所知，EATD-Corpus是第一个也是唯一包含中文音频和文本数据的公共凹陷数据集。在两个抑郁数据集上进行了评估，提出的方法实现了最新的性能。表现优于表现的结果表明了所提出的方法的有效性和概括能力。源代码和EATD-Corpus可在https://github.com/speechandlanguageprocessing/icassp2022-depression上找到。
Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants' interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method. The source code and EATD-Corpus are available at https://github.com/speechandlanguageprocessing/ICASSP2022-Depression.
