Optimizing Speech-Input Length for Speaker-Independent Depression Classification
Journal:
arXiv
Published Date:
Dec 31, 2024
Abstract
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.
