The ways in which vocal fold (VF) vibratory behavior and resulting voice quality (VQ) perception differ across
vowel categories and the co-articulatory variations of connected speech are unknown and largely unexplored.
Furthermore, phonatory adjustments during connected speech (possible variations in voicing onsets and
offsets and articulatory transitions) may provide important clinical information that can guide diagnostic
protocols, may correspond closely with perceived handicap, and may represent functionally-relevant
treatment targets. The overall goal of the proposed research is to discover and quantify physiological
mechanisms underlying normal and abnormal VF behavior and establish their relationships to VQ perception
in connected speech. This innovative proposal leverages the expertise of a multidisciplinary team and
develops a comprehensive framework linking vocal physiology and perception with improved measurement
approaches, methods, and analyses that account for the effects of co-articulation in connected speech.
Abnormalities in pre-, post-, and peri-phonatory vibratory behavior are physiological hallmarks of voice
disorders. Aim 1 will leverage precise, automated physiological measures of phonatory onset (pre-), offset
(post-), and variation in VF phase asymmetry (peri-) to characterize VF vibratory behavior in uniform vowel-
consonant-vowel (VCV) utterances with a controlled phonetic context. Aim 2 will establish relationship
between these physiological measures and dimension-specific VQ perception in VCV utterances, using ratio-
level matching tasks with physical units (e.g., dB) and biologically inspired computational models grounded
in psychoacoustics and auditory-perception. These evaluative methods overcome technical and
methodological limitations of conventional perceptual and acoustic methods. Aim 3 will evaluate and validate
the three physiological measures in connected speech and discover new physiological signatures currently
unknown through the use of novel and powerful machine-learning models that include as inputs physiological
measures derived from high-speed videoendoscopy. Aim 4 will use automated, efficient dimension-specific
computational models to evaluate VQ in connected speech and to discover physiological signatures that are
related to VQ perception through machine learning. The unique combination of machine learning with
computational models of VQ perception that are specific to VQ dimensions, rather than just overall severity,
can effectively deal with the massive data associated with connected speech and high-speed videoendoscopy.
Knowledge gained from this pre-translational research has the potential to improve our understanding of voice
pathology and to substantially advance functional assessment and treatment outcomes for millions of people
with hypo- and hyper-adductory voice disorders.