标题： 用于使用口语语言训练和评估抑郁风险模型的语料库大小要求 显示英文标题

标题： Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Language

摘要： 心理健康风险预测是语音领域的一个新兴领域，但许多研究都是基于小规模语料库进行的。 本研究说明了测试集和训练集大小的变化如何在受控研究中影响性能。 使用一个超过65K个标记数据点的语料库，提供了不同训练/测试集大小组合的完全交叉设计的结果。 包含两种模型类型：一种基于语言，另一种基于语音声学。 两者都使用了该领域当前的方法。 还包含了年龄不匹配的测试集。 结果表明，(1) 测试集大小低于1K样本时即使训练集较大也会产生噪声结果；(2) 至少需要2K的训练集大小才能得到稳定结果；(3) NLP和声学模型在训练/测试集大小变化时表现相似；(4) 不匹配的测试集表现出与匹配测试集相同的模式。 还讨论了其他因素，包括标签先验、模型强度和预训练、独特说话人以及数据长度。 尽管没有一项研究可以明确指定具体的大小要求，但结果表明，未来从语音和语言中进行心理健康风险预测的研究需要适当大小的训练和测试集。 显示英文摘要

摘要： Mental health risk prediction is a growing field in the speech community, but many studies are based on small corpora. This study illustrates how variations in test and train set sizes impact performance in a controlled study. Using a corpus of over 65K labeled data points, results from a fully crossed design of different train/test size combinations are provided. Two model types are included: one based on language and the other on speech acoustics. Both use methods current in this domain. An age-mismatched test set was also included. Results show that (1) test sizes below 1K samples gave noisy results, even for larger training set sizes; (2) training set sizes of at least 2K were needed for stable results; (3) NLP and acoustic models behaved similarly with train/test size variations, and (4) the mismatched test set showed the same patterns as the matched test set. Additional factors are discussed, including label priors, model strength and pre-training, unique speakers, and data lengths. While no single study can specify exact size requirements, results demonstrate the need for appropriately sized train and test sets for future studies of mental health risk prediction from speech and language.