Relationship Between Acoustic Voice Onset and Offset and Selected Instances of Oscillatory Onset and Offset in Young Healthy Men and Women

Abstract

Objective To investigate the relationship between (1) onset of the acoustic signal and pre-phonatory phases associated with oscillatory onset and (2) offset of the acoustic signal with the post-phonatory events associated with oscillatory offset across vocally healthy adults. Subjects and Methods High-speed videoendoscopy was captured simultaneously with the acoustic signal during repeated production of /hi.hi.hi/ at typical pitch and loudness from 56 vocally healthy adults (age 20–42 years; 21 male, 35 female). The relationship between the acoustic sound pressure signal and oscillatory onset /offset events from the glottal area waveforms (GAW), were statistically investigated using a multivariate linear regression analysis. Results The onset of the acoustic signal (X1a) is a significant predictor of the onset of first oscillations (X1g) and onset of sustained oscillations (X2g). X1a as well as gender are significant predictors of the first instance of medial contact (X1.5g). The offset of the acoustic signal (X2a) is a significant predictor of the first instance of oscillatory offset (X3g), first instance of incomplete glottal closure (X3.5g), and cessation of vocal fold motion (X4g). Conclusions The acoustic signal onset is closely related to the first medial contact of the vocal folds but the latency between these events is longer for females compared to males. The offset of the acoustic signal occurs immediately after incomplete glottal adduction. The emerging normative group latencies between the onset/offset of the acoustic and the GAW from this study appear promising for future investigations.

Oscillatory Onset and Offset in Young Vocally Healthy Adults Across Various Measurement Methods

“…Human subjects data from our prior study (Patel et al, in press) 32 was used for this current study. In our prior study we investigated the relationship between the onset and offset of the acoustic signal with various events associated with oscillatory onset and offset of the vocal folds.…”

“…Additionally, the current study has more number of participants compared to our prior study with 56 vocally healthy adults (male = 21, female =35). 32 Ninety-two subjects (21–45 years) were recruited at the Vocal Physiology and Imaging Lab at the University of Kentucky after signing of the Institutional Review Board approved informed consent form. All participants were perceptually judged to have a normal voice on the GRBAS 33 scale by a speech language pathologist specializing in the area of voice.…”

Analysis of phonation onsets in vowel production, using information from glottal area and flow estimate

“…Although their results indicate a correspondence between VAT and manually extracted onset duration in HSV data, obtaining reliable EGG signal can be challenging. Patel et al (2017a) compared three manually extracted time instants in HSV data (first detected oscillation of vocal folds, first medial vocal fold contact, and sustained phonation) to the first periodic deviation in the acoustic signal. Their results indicate a quantifiable relationship between onsets in HSV data and acoustic signals, but the manual extraction of the time instants is subject to human error and judgment as well as to noise.…”

“…Direct observation of this rate of change can only be done using visual means, such as HSV and videokymography (Švec and Schutte, 1996). These visual means, HSV in particular, are irreplaceable in both clinical and research work, and several studies have used HSV to study the onset of phonation (e.g., Mergell et al, 1998;Braunschweig et al, 2008;Patel et al, 2017a). However, the invasiveness of HSV and the expertise required of the experimenter impose restrictions for its use.…”

Interaction Mechanisms between Glottal Source and Vocal Tract in Pitch Glides

“…Although recent research emphasis is on developing fully automated segmentation to extract the glottal area from HSV image sequences, semi-automatic methods remain widely used. For example, the adapted seeded region growing algorithm developed by Lohscheller et al (2007), which uses manually picked seed points, and a spatially and temporally varying threshold as the region membership criterion, has been used by, for example, Aichinger et al (2017), Döllinger et al (2017), and Patel et al (2017a), and included in HSV data analysis toolkits such as GlottalImageExplorer (Birkholz, 2016).…”