标题： 优化语音输入长度以进行独立于说话人的抑郁分类 显示英文标题

标题： Optimizing Speech-Input Length for Speaker-Independent Depression Classification

摘要： 基于语音的抑郁症分类机器学习模型为医疗应用提供了希望。 尽管在抑郁症分类方面有越来越多的研究，但关于语音输入长度如何影响模型性能的理解仍然有限。 我们分析了使用来自人机健康筛查应用的超过1400小时语音语料库进行的独立说话人抑郁症分类结果。 我们研究了两种在整体性能上有所不同的自然语言处理系统的性能，作为响应输入长度的函数。 两种系统的结果显示，性能取决于自然长度、经过时间长度以及响应在会话中的顺序。 系统共享一个最小长度阈值，但在响应饱和阈值上有所不同，较好的系统该阈值更高。 在饱和时，向说话人提出新问题比继续当前响应更好。 这些以及额外报告的结果表明，应用程序可以更好地设计以激发和处理用于抑郁症分类的最佳输入长度。 显示英文摘要

摘要： Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression classification, little is understood about how the length of speech-input impacts model performance. We analyze results for speaker-independent depression classification using a corpus of over 1400 hours of speech from a human-machine health screening application. We examine performance as a function of response input length for two NLP systems that differ in overall performance. Results for both systems show that performance depends on natural length, elapsed length, and ordering of the response within a session. Systems share a minimum length threshold, but differ in a response saturation threshold, with the latter higher for the better system. At saturation it is better to pose a new question to the speaker, than to continue the current response. These and additional reported results suggest how applications can be better designed to both elicit and process optimal input lengths for depression classification.