D3TEC Dataset

Depression is a mental health condition that affects millions of people worldwide. Although com- mon, it remains difficult to diagnose due to its heterogeneous symptomatology. Mental health questionnaires are currently the most used assessment method to screen depression; these, how- ever, have a subjective nature due to their dependence on patients’ self-assessments. Researchers have been interested in finding an accurate way of identifying depression through an objective biomarker. Recent developments in neural networks and deep learning have enabled the possi- bility of classifying depression through the computational analysis of voice recordings. However, this approach is heavily dependent on the availability of datasets to train and test deep learning models, and these are scarce. There are also very few languages available. This study proposes a protocol for the collection of a new dataset for deep learning research on voice depression classifi- cation, featuring Spanish speakers, professional and smartphone microphones, and a high-quality recording standard. This work aims at creating a high-quality voice depression dataset by recording Spanish speakers with a professional microphone and strict audio quality standards. The data is captured by a smartphone microphone as well for further research in the use of smartphone applications for depression identification. Our methodology involves the strategic collection of depressed and non-depressed voice recordings. Three types of data are collected: voice recordings, depression labels (using the PHQ-9 questionnaire), and additional data that could potentially influence speech. Recordings are captured with professional-grade and smartphone microphones simultaneously to ensure versatility and practical applicability. Several considerations and guidelines are described to ensure high audio quality and avoid potential bias in deep learning research. This data collection effort immediately enables new research topics on depression classifica- tion. Some potential uses include deep learning research on Spanish speakers, an evaluation of the impact of audio quality on developing audio classification models, and an evaluation of the appli- cability of voice depression classification technology on smartphone applications. A preliminary experimentation section is included to showcase the potential research areas that the creation of this dataset enables. This research marks a significant step towards the objective and automated classification of depression in voice recordings. By focusing on the underrepresented demographic of Spanish speakers, the inclusion of smartphone recordings, and addressing the current data limitations in audio quality, this study lays the groundwork for future advancements in deep learning-driven mental health diagnosis.

抑郁症是一种影响全球数百万人的心理健康状况。尽管其症状表现异质性较高，但仍普遍存在诊断困难的问题。目前，心理健康问卷是用于筛查抑郁症最常用的评估方法；然而，由于这些问卷依赖于患者的自我评估，因此其主观性较强。研究人员一直致力于寻找一种通过客观生物标志物准确识别抑郁症的方法。近年来，神经网络和深度学习的发展使得通过语音记录的计算机分析来对抑郁症进行分类成为可能。然而，这种方法严重依赖于数据集的可用性，以供训练和测试深度学习模型，而这些数据集却相对稀缺。此外，可用的语言种类也非常有限。本研究提出了一种新的数据集收集方案，旨在进行深度学习在语音抑郁症分类方面的研究，该方案涉及西班牙语使用者、专业和智能手机麦克风，以及高质量的录音标准。本研究旨在通过使用专业麦克风并严格遵守音频质量标准来记录西班牙语使用者的语音，从而创建一个高质量的语音抑郁症数据集。同时，为了进一步研究智能手机应用程序在抑郁症识别中的应用，数据也通过智能手机麦克风捕捉。我们的方法包括对抑郁症和非抑郁症语音记录的战略性收集。收集了三种类型的数据：语音记录、抑郁症标签（使用PHQ-9问卷），以及可能影响语音的其他数据。录音同时使用专业级和智能手机麦克风进行，以确保多样性和实用性。本研究还描述了若干考虑因素和指导方针，以确保高音频质量并避免深度学习研究中的潜在偏差。这一数据收集工作即刻为抑郁症分类的新研究领域提供了可能性。其潜在用途包括针对西班牙语使用者的深度学习研究、评估音频质量对构建音频分类模型的影响，以及评估语音抑郁症分类技术在智能手机应用程序中的应用。研究还包括一个初步实验部分，以展示创建该数据集所启发的潜在研究领域。本研究标志着语音记录中抑郁症客观和自动化分类的重要一步。通过关注代表性不足的西班牙语使用者群体、包含智能手机录音以及解决当前音频质量数据限制，本研究为深度学习驱动下的心理健康诊断的未来发展奠定了基础。

搜集汇总

背景与挑战

背景概述

D3TEC Dataset是一个专注于抑郁症自动诊断的语音数据集，包含西班牙语使用者的高质量音频记录（专业和智能手机麦克风）和抑郁症标签。该数据集旨在支持深度学习研究，特别是在抑郁症分类和智能手机应用中的语音分析技术。