Optimizing Speech-Input Length for Speaker-Independent Depression Classification

TL;DR

This study investigates how the length of speech inputs affects the accuracy of speaker-independent depression classification models, revealing optimal input lengths and strategies for improved health screening applications.

Contribution

It provides the first detailed analysis of speech input length impact on depression classification performance, guiding better design of speech-based health screening systems.

Findings

Performance depends on natural and elapsed response length.

A minimum input length threshold is necessary for accurate classification.

Better system performance correlates with higher response saturation thresholds.

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.