  This first-of-its-kind 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. In addition, a novel non-local acoustic
spectrogram feature extraction network identifies meaningful acoustic
attributes. Finally, a sequential network utilizes the acoustic attributes and
the extracted temporal speech importance for effective classification. We
collected speech and physiological sensing data from 73 preschool-age children
who stutter (CWS) and who don't stutter (CWNS) in different conditions. PASAD's
unique architecture enables visualizing speech attributes distinct to a CWS's
fluent speech and mapping them to the speaker's respective speech-motor-control
factors (i.e., speech articulators). Extracted knowledge can enhance
understanding of children's fluent speech, speech-motor-control (SMC), 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 on mobile and scalable
devices.

Sound;Human-Computer Interaction;Audio and Speech Processing