Psychophysiology-aided Perceptually Fluent Speech Analysis of Children Who Stutter

TL;DR

This paper introduces PASAD, a novel real-time approach that uses physiological responses to analyze and identify speech-motor-control factors in children who stutter, improving understanding and detection of fluency issues.

Contribution

The paper presents a new PASAD method leveraging physiological data and a Hyper-Network architecture to analyze perceptually fluent speech in children who stutter, outperforming existing approaches.

Findings

PASAD outperforms state-of-the-art baselines.

It is robust and generalizable across conditions.

It enables real-time analysis of speech and physiology.

Abstract

This paper presents a novel approach named PASAD that detects changes in perceptually fluent speech acoustics of young children. Particularly, analysis of perceptually fluent speech enables identifying the speech-motor-control factors that are considered as the underlying cause of stuttering disfluencies. Recent studies indicate that the speech production of young children, especially those who stutter, may get adversely affected by situational physiological arousal. A major contribution of this paper is leveraging the speaker's situational physiological responses in real-time to analyze the speech signal effectively. The presented PASAD approach adapts a Hyper-Network structure to extract temporal speech importance information leveraging physiological parameters. Moreover, we collected speech and physiological sensing data from 73 preschool-age children who stutter (CWS) and who do not stutter (CWNS) in different conditions. PASAD's unique architecture enables identifying speech attributes distinct to a CWS's fluent speech and mapping them to the speaker's respective speech-motor-control factors. Extracted knowledge can enhance understanding of children's speech-motor-control and stuttering development. Our comprehensive evaluation shows that PASAD outperforms state-of-the-art multi-modal baseline approaches in different conditions, is expressive and adaptive to the speaker's speech and physiology, generalizable, robust, and is real-time executable.