Skewing of the glottal flow with respect to the glottal area measured in natural production of vowels

Abstract

In the production of voiced speech, glottal flow skewing refers to the tilting of the glottal flow pulses to the right, often characterized as a delay of the peak, compared to the glottal area. In the past four decades, several studies have addressed this phenomenon using modeling of voice production with analog circuits and computer simulations. However, previous studies measuring flow skewing in natural production of speech are sparse and they contain little quantitative data about the degree of skewing between flow and area. In the current study, flow skewing was measured from the natural production of 40 vowel utterances produced by 10 speakers. Glottal flow was measured from speech using glottal inverse filtering and glottal area was captured with high-speed videoendoscopy. The estimated glottal flow and area waveforms were parameterized with four robust parameters that measure pulse skewness quantitatively. Statistical tests obtained for all four parameters showed that the flow pulse was significantly more skewed to the right than the area pulse. Hence, this study corroborates the existence of flow skewing using measurements from natural speech production. In addition, the study yields quantitative data about pulse skewness in simultaneous measured glottal flow and area in natural production of speech.

Correlating Perceptual Voice Quality in Adductor Spasmodic Dysphonia With Computer Vision Assessment of Glottal Geometry Dynamics

“…Furthermore, the exact size of the image and the phonatory step within the laryngoscope video do not matter. On the contrary, many computer vision/machine learning algorithms that aim to segment the glottis or vocal folds have several requirements, such as a specific image resolution (usually around 256 × 256 pixels; Alku et al, 2019;Fehling et al, 2020;Wurzbacher et al, 2008), the high frame rates associated with HSV, and sometimes manual intervention to handpick frames that depict the glottis or certain laryngeal characteristics. Our system is not limited by strict specifications regarding video quality or time within a nasolaryngoscopic video.…”

“…Once segmented, various geometric measures that characterize the shape can be easily computed. These segmentation methods have included edge detection and region growing algorithms (Alku et al, 2019;Chen et al, 2013), pixel thresholding (Wurzbacher et al, 2006), a watershed transform and a linear predictor (Osma-Ruiz et al, 2008), a convolutional neural network-based semantic segmentation (Laves et al, 2019), and a deep convolutional long short-term memory network (Fehling et al, 2020). Many of these studies use high-speed video (HSV) recordings of the larynx.…”

Extraction and Utilization of Excitation Information of Speech: A Review

“…are many recent investigations where GIF methods have been studied jointly with glottal area information extracted using HSV or with physical models of voice production. These investigations have addressed issues, such as the relationship between the glottal flow and glottal area in the presence of source-filter interaction [76], [77] and in phonation onsets [55], the computation of parameter values for physical models [78], [79], and the estimation of subglottal pressure, laryngeal muscle activation, and vocal fold contact pressure [80]. We argue that the strategy used in these investigations to study excitation information of speech (i.e., using GIF jointly with HSV and with physical modeling approaches) will become increasingly important and also increasingly feasible in the future due to the progress in HSV [50], [81], physical modeling [82], [83], and GIF [59], [67], [71].…”