Electroglottographic Signals Research Articles

Relation of RBH Auditory-Perceptual Scale to Acoustic and Electroglottographic Voice Analysis in Children With Vocal Nodules

The aim of this paper was to present an analysis of the feasibility of voice quality prediction on the roughness, breathiness, hoarseness (RBH) scale for children with vocal nodules on the basis of both acoustic parameters and electroglottographic (EGG) examination. The first step to achieve this goal involved the creation of a dedicated database, Voice Pathology Analysis Database (VPADB), containing voice recordings from patients, the EGG signal, medical diagnosis, and the classification of voice quality on the above-mentioned scale. The database also contains data concerning the patients' age and sex. The next step involved performing statistical analyses to test the relationship between the values of objective parameters, such as peak slope and normalized amplitude quotient, and the classification of voice quality. The study made use of voice recordings of 57 patients with vocal nodules and 37 healthy individuals. The RBH classification was carried out by two independent voice specialists. It was found that speech signal parameters can be used to predict expert evaluation with regard to roughness and hoarseness.

Follow-up analysis of voice quality in patients with late-onset Pompe disease

BackgroundLate-onset Pompe disease (LOPD) is a metabolic myopathy disorder characterized by progressive muscle damage and among others dysfunction of the voice apparatus, which affects speech and – above all – voice quality. Symptoms include dysphonia, instability, glottic insufficiency, and tense voice. The aim of this study was to evaluate and compare voice quality disorder in a group of 15 LOPD patients who were first examined in 2014 and then re-examined in 2017.MethodsIn both 2014 and 2017, the same 15 LOPD patients, ranging in age from 15 to 57, from 10 different families, underwent the following examinations: perceptual assessment of voice quality on the RBH scale, electroglottographic recordings, and acoustic recordings. All the patients were on enzyme replacement therapy (ERT).ResultsThree years after the 2014 study, the LOPD patients demonstrated a deterioration in voice quality. A statistically significant increase in glottic insufficiency (p = 0.0399) and a shift towards tense voice (p = 0.0417) were observed. Two patients – out of three who had received presymptomatic treatment – demonstrated stable voice quality compared with 2014.ConclusionsThe results suggest increased muscle weakness and progression of LOPD. The parameters Closed Quotient (calculated on the basis of an electroglottographic signal) and Peak Slope (calculated on the basis of an acoustic signal) proved to be the most sensitive.

Investigating Laryngeal “Tilt” on Same-pitch Phonation—Preliminary Findings of Vocal Mode Metal and Density Parameters as Alternatives to Cricothyroid-Thyroarytenoid “Mix”

The objective of this work was to study the parameters "metal" and "density" and their combinatory conditions in relation to the vocal modes Overdrive, Edge, and Curbing from the pedagogical method Complete Vocal Technique by means of laryngostroboscopic imaging, high-speed videoendoscopy, electroglottography (EGG), long-time-average spectrum (LTAS), and acoustics. This is a three-subject exploratory study. Three singers were recorded performing various metal and density conditions of Overdrive, Edge, and Curbing from the Complete Vocal Technique method by means of laryngostroboscopic imaging using a videonasoendoscopic camera system and the Laryngostrobe program, high-speed videoendoscopy, EGG, LTAS, and acoustic signals using SpeechStudio. A laryngeal tilt was discovered as related to the condition of "reduced density" on same-pitch phonation, with observations of the thyroid cartilage tilting forward, stretching of the mucosa covering the cricoid-arytenoid complex and the posterior cricoid, and an upward posterior, slightly superior, contraction of the middle constrictor muscle in the pharyngeal wall. A resulting associated reduced contact quotient was observed on EGG, as well as a lowered mean sound pressure level. On LTAS, the laryngeal tilt was associated with a decrease of spectral energy in areas between the 7th and the 20th harmonic. The subjects of the study were able to perform the tilt without changing pitch or volume. Singers can perform laryngeal tilt during same-pitch phonation in the vocal modes Overdrive, Edge, and Curbing. The parameters of density and metal establish a more precise and anatomically grounded terminology than "mix register."

Fundamental Frequency Estimation of Low-quality Electroglottographic Signals

Fundamental frequency (fo) is often estimated based on electroglottographic (EGG) signals. Because of the nature of the method, the quality of EGG signals may be impaired by certain features like amplitude or baseline drifts, mains hum, or noise. The potential adverse effects of these factors on fo estimation have to date not been investigated. Here, the performance of 13 algorithms for estimating fo was tested, based on 147 synthesized EGG signals with varying degrees of signal quality deterioration. Algorithm performance was assessed through the standard deviation σfo of the difference between known and estimated fo data, expressed in octaves. With very few exceptions, simulated mains hum, and amplitude and baseline drifts did not influence fo results, even though some algorithms consistently outperformed others. When increasing either cycle-to-cycle fo variation or the degree of subharmonics, the SIGMA algorithm had the best performance (max. σfo = 0.04). That algorithm was, however, more easily disturbed by typical EGG equipment noise, whereas the NDF and Praat's auto-correlation algorithms performed best in this category (σfo = 0.01). These results suggest that the algorithm for fo estimation of EGG signals needs to be selected specifically for each particular data set. Overall, estimated fo data should be interpreted with care.

Epoch Estimation from Emotional Speech Signals Using Variational Mode Decomposition

This paper presents a novel approach for the estimation of epochs from the emotional speech signal. Epochs are the locations of significant excitation in the vocal tract during the production of voiced sound by the vibration of vocal folds. The estimation of epoch locations is essential for deriving instantaneous pitch contours for accurate emotion analysis. Many well-known algorithms for epoch extraction are found to show degraded performance due to the varying nature of excitation characteristics in the emotional speech signal. The proposed approach exploits the effectiveness of a new adaptive time series decomposition technique called variational mode decomposition (VMD) for the estimation of epochs. The VMD algorithm is applied on the emotional speech signal for decomposition of the signal into various sub-signals. Analysis of these signals shows that the VMD algorithm captures the center frequency close to the fundamental frequency defined for each glottal cycle of emotional speech utterance through its modes. This center frequency characteristic of the corresponding mode signal helps in the accurate estimation of epoch locations from the emotional speech signal. The performance evaluation of the proposed method is carried out on six different emotions taken from the German emotional speech database with simultaneous electroglottographic signals. Experimental results on clean emotive speech signals show that the proposed method provides identification rate and accuracy comparable to that of the best performing algorithm. Besides, the proposed method provides better reliability in epoch estimation from emotive speech signals degraded by the presence of noise.

Quantifying syringeal dynamics in vitro using electroglottography.

The complex and elaborate vocalizations uttered by many of the 10,000 extant bird species are considered a major driver in their evolutionary success, warranting study of the underlying mechanisms of vocal production. Additionally, birdsong has developed into a highly productive model system for vocal imitation learning and motor control, where, in contrast to humans, we have experimental access to the entire neuromechanical control loop. In human voice production, complex laryngeal geometry, vocal fold tissue properties, airflow and laryngeal musculature all interact to ultimately control vocal fold kinematics. Quantifying vocal fold kinematics is thus critical to understanding neuromechanical control of voiced sound production, but in vivo imaging of vocal fold kinematics in birds is experimentally challenging. Here, we adapted and tested electroglottography (EGG) as a novel tool for examining vocal fold kinematics in the avian vocal organ, the syrinx. We furthermore imaged and quantified syringeal kinematics in the pigeon (Columba livia) syrinx with unprecedented detail. Our results show that EGG signals predict (1) the relative amount of contact between the avian equivalent of vocal folds and (2) essential parameters describing vibratory kinematics, such as fundamental frequency, and timing of syringeal opening and closing events. As such, EGG provides novel opportunities for measuring syringeal vibratory kinematic parameters in vivo Furthermore, the opportunity for imaging syringeal vibratory kinematics from multiple planar views (horizontal and coronal) simultaneously promotes birds as an excellent model system for studying kinematics and control of voiced sound production in general, including in humans and other mammals.

Periodicity, spectral and electroglottographic analyses of pressed voice in expressive speech

Pressed voice is a type of voice quality produced by pressing/straining the vocal folds, which often appears in Japanese conversational speech when expressing paralinguistic information related to emotional or attitudinal behaviors of the speaker. With the aim of clarifying the acoustic and physiological features involved in pressed voice production, we conducted periodicity, spectral and electroglottographic (EGG) analyses on pressed voice segments extracted from spontaneous dialogue speech of several speakers. Periodicity analysis first indicated that pressed voice is usually accompanied by creaky or harsh voices, having irregularities in periodicity, but can also be accompanied by periodic voices with fundamental frequencies in the range of modal phonation. Spectral analysis indicated power is usually reduced in low frequency components of pressed segments. A spectral measure H1'-A1' was then proposed for characterizing pressed voice segments which commonly has few or no harmonicity. H1'-A1' was shown to be effective for identifying most pressed segments, but fails when nasalization occurs. Vocal fold vibratory pattern analysis from the EGG signals revealed that most pressed voice segments (including nasalized vowels) are characterized by glottal pulses with closed intervals longer than open intervals on average, regardless of periodicity.

Non-invasive documentation of primate voice production using electroglottography

Electroglottography (EGG) is a low-cost, non-invasive method for documenting laryngeal sound production during vocalization. The EGG signal represents relative vocal-fold contact area and thus delivers physiological evidence of vocal-fold vibration. While the method has received much attention in human voice research over the last five decades, it has seen very little application in other mammals. Here, we give a concise overview of mammalian vocal production principles. We explain how mammalian voice production physiology and the dynamics of vocal-fold vibration can be documented qualitatively and quantitatively with EGG, and we summarize and discuss key issues from research with humans. Finally, we review the limited number of studies applying EGG to non-human mammals, both in vivo and in vitro. The potential of EGG for non-invasive assessment of non-human primate vocalization is demonstrated with novel in vivo data of Cebus albifrons and Ateles chamek vocalization. These examples illustrate the great potential of EGG as a new minimally invasive tool in primate research, which can provide important insight into the ‘black box’ that is vocal production. A better understanding of vocal-fold vibration across a range of taxa can provide us with a deeper understanding of several important elements of speech evolution, such as the universality of vocal production mechanisms, the independence of source and filter, the evolution of vocal control, and the relevance of non-linear phenomena.

Voice alterations in patients with Morquio A syndrome

Morquio A syndrome, or mucopolysaccharidosis (MPS IV A), is an inherited lysosomal storage disorder which belongs to the group of mucopolysaccharidoses (MPSs). It is caused by N-acetylgalactosamine-6-sulfatase (GALNS) activity deficiency, which results in impaired degradation of glycosaminoglycans (GAGs), including keratan sulfate (KS) and chondroitin-6-sulfate (CS). These compounds infiltrate and disrupt the architecture of the extracellular matrix, compromising the integrity of the connective tissue. Patients with Morquio A have also been noted for exhibiting abnormalities of the larynx and vocal tract. The aim of the study was to assess voice alterations using noninvasive acoustic and electroglottographic voice analysis. Electroglottographic signal and acoustic analyses revealed considerable changes in the voices of patients with Morquio A syndrome when compared to the voices of healthy controls. Affected patients tended toward tense voice, incomplete glottal closure, increased incidence of vocal fold nodules, dysphonia, and hoarse voice. Morquio A syndrome is characterized by connective tissue disease, which adversely affects voice quality. The use of objective voice analysis makes it possible to quantitatively monitor changes in the vocal apparatus over the course of disease progression, and also allows for assessment of the effects of the enzyme replacement therapy.

On simultaneous electromagnetic articulography and electroglottography data acquisition

Simultaneous measurements of speech articulator movement and laryngeal activity are desirable for obtaining a picture of the coarticulatory behavior between oral articulators and laryngeal vocal fold behavior. However, since electroglottography (EGG) use the application of a weak current across the larynx, which is in close proximity to the oral vocal tract, there is a possibility that its use may interfere with the sensor voltage fluctuation measurements used in electromagnetic articulography (EMA). In order to investigate any hypothetical interference effect, data was collected using Northern Digital Instruments’ Wave articulograph with Glottal Enterprises’s EG2-PCX electroglottograph. Datasets were collected from one male and one female speaker—both with simultaneous EMA and EGG and, for comparison, with EMA alone. Means and standard deviations of inter-sensor distances for static (i.e., reference) and moving (e.g., tongue tip, lip, and jaw) EMA sensor trajectories were compared across the two collection conditions for possible interference effects. Preliminary results find no discernable differences in inter-sensor distances for either static or moving EMA sensors between the with- and without-EGG conditions. If this finding is maintained for additional speakers, NDI EMA data and GE EGG signals may be collected simultaneously without adverse effects on the measurement. [Work supported by NIH.]

Significance of Differenced EGG Signal as a Spectrum in Phase Difference Computation for the Estimation of Glottal Closure Instants

Estimation of glottal closure instants (GCIs) from an electroglottograph (EGG) signal can aid in clinical applications involving the diagnosis and treatment of speech pathologies and can also serve as a ground truth to assess algorithms that estimate GCIs from speech signals. In this regard, the current work proposes a phase-difference-based approach that considers the symmetrized, differenced EGG (DEGG) signal to be the Fourier transform of an arbitrary even-signal, to estimate GCIs from EGG signals. The DEGG signal possesses sharp negative valleys at the GCIs and since the symmetrized DEGG is assumed to be a spectrum, these valleys correspond to zeros that lie outside the unit circle. The angular locations of these zeros, and in turn the locations of GCIs, can be derived from the phase-difference spectrum, since it possesses a value of around $$2\pi $$2? at these locations, the derivation of which is elaborated in the paper. The proposed algorithm is compared with the existing time of excitation generator, the high quality time of excitation algorithm, and the singularity in EGG by multiscale analysis algorithm, in terms of the identification, miss, and false alarm rates, and the identification accuracy, on normal and pathological EGG. The proposed algorithm is observed to outperform the rest with an identification rate of 98.28% in normal EGG and 96.90% in pathological EGG.

Significance of incorporating excitation source parameters for improved emotion recognition from speech and electroglottographic signals

The work presented in this paper explores the effectiveness of incorporating the excitation source parameters such as strength of excitation and instantaneous fundamental frequency ( $$F_0$$ ) for emotion recognition task from speech and electroglottographic (EGG) signals. The strength of excitation (SoE) is an important parameter indicating the pressure with which glottis closes at the glottal closure instants (GCIs). The SoE is computed by the popular zero frequency filtering (ZFF) method which accurately estimates the glottal signal characteristics by attenuating or removing the high frequency vocaltract interactions in speech. The arbitrary impulse sequence, obtained from the estimated GCIs, is used to derive the instantaneous $$F_0$$ . The SoE and the instantaneous $$F_0$$ parameters are combined with the conventional mel frequency cepstral coefficients (MFCC) to improve the recognition rates of distinct emotions (Anger, Happy and Sad) using Gaussian mixture models as classifier. The performances of the proposed combination of SoE and instantaneous $$F_0$$ and their dynamic features with MFCC coefficients are compared with the emotion utterances (4 emotions and neutral) from classical German full blown emotion speech database (EmoDb) having simultaneous speech and EGG signals and Surrey Audio Visual Expressed Emotion database (3 emotions and neutral) for both speaker dependent and speaker independent emotion recognition scenarios. To reinforce the effectiveness of the proposed features and for better statistical consistency of the emotion analysis, a fairly large emotion speech database of 220 utterances per emotion in Tamil language with simultaneous EGG recordings, is used in addition to EmoDb. The effectiveness of SoE and instantaneous $$F_0$$ in characterizing different emotions is also confirmed by the improved emotion recognition performance in Tamil speech-EGG emotion database.

Intelligent acoustic data fusion technique for information security analysis

Tone is an essential component of word formation in all tonal languages, and it plays an important role in the transmission of information in speech communication. Therefore, tones characteristics study can be applied into security analysis of acoustic signal by the means of language identification, etc. In speech processing, fundamental frequency (F0) is often viewed as representing tones by researchers of speech synthesis. However, regular F0 values may lead to low naturalness in synthesized speech. Moreover, F0 and tone are not equivalent linguistically; F0 is just a representation of a tone. Therefore, the Electroglottography (EGG) signal is collected for deeper tones characteristics study. In this paper, focusing on the Northern Kam language, which has nine tonal contours and five level tone types, we first collected EGG and speech signals from six natural male speakers of the Northern Kam language, and then achieved the clustering distributions of the tone curves. After summarizing the main characteristics of tones of Northern Kam, we analyzed the relationship between EGG and speech signal parameters, and laid the foundation for further security analysis of acoustic signal.

Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.

Measurement of the Maximum Frequency of Electroglottographic Fluctuations in the Expiration Phase of Volitional Cough as a Functional Test for Cough Efficiency

The hypotheses of the present study were that the maximum frequency of fluctuation of electroglottographic (EGG) signals in the expiration phase of volitional cough (VC) reflects the cough efficiency and that this EGG parameter is affected by impaired laryngeal closure, expiratory effort strength, and gender. For 20 normal healthy adults and 20 patients diagnosed with unilateral vocal fold paralysis (UVFP), each participant was fitted with EGG electrodes on the neck, had a transnasal laryngo-fiberscope inserted, and was asked to perform weak/strong VC tasks while EGG signals and a high-speed digital image of the larynx were recorded. The maximum frequency was calculated in the EGG fluctuation region coinciding with vigorous vocal fold vibration in the laryngeal HSDIs. In addition, each participant underwent spirometry for measurement of three aerodynamic parameters, including peak expiratory air flow (PEAF), during weak/strong VC tasks. Significant differences were found for both maximum EGG frequency and PEAF between the healthy and UVFP groups and between the weak and strong VC tasks. Among the three cough aerodynamic parameters, PEAF showed the highest positive correlation with the maximum EGG frequency. The correlation coefficients between the maximum EGG frequency and PEAF recorded simultaneously were 0.574 for the whole group, and 0.782/0.717/0.823/0.688 for the male/female/male-healthy/male-UVFP subgroups, respectively. Consequently, the maximum EGG frequency measured in the expiration phase of VC was shown to reflect the velocity of expiratory airflow to some extent and was suggested to be affected by vocal fold physical properties, glottal closure condition, and the expiratory function.

Accurate Estimation of Glottal Closure Instants and Glottal Opening Instants from Electroglottographic Signal Using Variational Mode Decomposition

The objective of the proposed work is to accurately estimate the glottal closure instants (GCIs) and glottal opening instant (GOIs) from electroglottographic (EGG) signals. This work also addresses the issues with existing EGG-based GCI/GOI detection methods. GCIs are the instants at which excitation to the vocal tract is maximum and GOIs, on the other hand, have minimum excitation compared to GCIs. Both these instants occur instantaneously with a fundamental frequency defined for each glottal cycle in a given EGG signal. Accurate detection of these instants from the EGG signal is essential for the performance evaluation of GCIs and GOIs estimated from the speech signal directly. This work proposes a new method for accurate detection of GCIs and GOIs from the EGG signal using variational mode decomposition (VMD) algorithm. The EGG signal has been decomposed into sub-signals using the VMD algorithm. It is shown that VMD captures the center frequency close to the fundamental frequency of the EGG signal through one of its modes. This property of the corresponding mode helps to estimate GCIs and GOIs from the same. Besides, instantaneous pitch frequency is estimated from the obtained GCIs. The proposed method has been evaluated on the CMU-arctic database for GCI/GOI estimation and the Keele pitch extraction reference database for instantaneous pitch frequency estimation. The effectiveness of the proposed method is confirmed by comparison with state-of-the-art methods. Experimental results show that the proposed method has better accuracy and identification rate compared to state-of-the-art methods.

Contact Quotient of Female Singers Singing Four Pitches for Five Vowels in Normal and Pressed Phonations

The present study aimed to investigate the contact quotient (CQ) values of breathy, normal, and pressed phonation types in four different sections of the female singing range. Electroglottography (EGG) and acoustic signals were recorded from 10 female singing teachers. Five vowels were sung for 1-3 seconds each, in three phonation types-normal, breathy, and pressed, in four pitches representing registration change points in the singing range. CQ values were automatically generated from the EGG signal using VoceVista at 35% threshold level. Sound pressure levels were checked in Praat. Unianova and correlations were performed using an SPSS program. CQ values of female participants in the study yielded ranges of 0.25-0.62 in normal and 0.34-0.73 in pressed. Normal and pressed CQ differed significantly from each other at P < 0.00. Breathy samples were not viable for analysis. A concentration of CQ values from 0.5 to 0.6 of both pressed low and pressed break samples was noted, but CQ values across the pitch range showed no significant trend. Normal and pressed phonation CQ values beyond the speaking pitch varied among the subjects. Pressed phonation CQ values were mostly higher, but the values were only relative to the corresponding normal phonation on the same pitch. Other measurements may be more suitable for measuring vocal fold impact stress in higher frequencies, thus, distinguishing normal from pressed singing.

Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types

Abstract This study compared for the electroglottographic (EGG) signal how well six earlier presented and two new parameters distinguish between normal, breathy and pressed phonation and how well they correlate with perceptual evaluation. The results were compared with those obtained for nine parameters describing the glottal flow waveform obtained through inverse filtering of the acoustic speech pressure signal. Acoustic and dual-channel EGG signals were recorded for twenty female and twenty male subjects with healthy voices phonating sustained samples of the vowel [a:] in their habitual normal voice and in simulated breathy (hypofunctional) and pressed (hyperfunctional) phonation. The samples were perceptually evaluated by five voice specialists and rated for firmness of phonation. The best examples from 12 females and 12 males were used for the analyses. Few earlier studies have ranked the behavior of this many EGG and glottal flow parameters from this large speech data. Although the parameters differed in their ranking order, contact quotient calculated with a criterion level at 50% both from the EGG and the inverse filtered signal was strong in correlating with perception and in distinguishing phonation types in cases where fundamental frequency and sound pressure level also varied. When this variation was taken into account, the normalized amplitude quotient NAQ still had an effect in predicting voice quality. The results will have applicability in voice training and therapy and in development of machine learning -based classification methods.

Robust glottal activity detection using the phase of an electroglottographic signal

Abstract Electroglottography (EGG) is a non-invasive technique of studying the glottal activity. The phase of EGG signal has not attracted much attention, although it is robust and capable of revealing dynamics of glottal activity. The main objective of this work is to develop a robust and accurate technique for analyzing the glottal activity by exploiting the phase of EGG signal. Using the phase of EGG signal, accurate and robust methods are proposed to detect glottal closure instant (GCI) and glottal opening instant (GOI) within a glottal cycle. The glottal activity during close and open phases of a glottal cycle can be precisely analysed by using GCIs and GOIs respectively. Robustness of the proposed method for detection of GCIs and GOIs is also evaluated in vulnerable cases such as transition in laryngeal mechanism, weak voicing and voice transition. The accuracy of the proposed method is evaluated using manually labeled EGG signals chosen from CMU Arctic database, and the performance of the proposed method is also compared with the state-of-the-art singularity in EGG by multiscale analysis (SIGMA) method. Finally, the performance of the proposed and SIGMA methods is also evaluated on EGG signals recorded from the patients having vocal folds disorders.

Curbing—The Metallic Mode In-between: An empirical study qualifying and categorizing restrained sounds known as Curbing based on audio perception, laryngostroboscopic imaging, acoustics, LTAS, and EGG

This study aims to study the categorization Curbing from the pedagogical method Complete Vocal Technique as a reduced metallic mode compared with the full metallic modes Overdrive and Edge by means of audio perception, laryngostroboscopic imaging, acoustics, long-term average spectrum (LTAS), and electroglottography (EGG). Twenty singers were recorded singing sustained vowels in a restrained character known as Curbing. Two studies were performed: (1) laryngostroboscopic examination using a videonasoendoscopic camera system and the Laryngostrobe program; and (2) simultaneous recording of EGG and acoustic signals using Speech Studio. Images were analyzed based on consensus agreement. Statistical analysis of acoustic, LTAS, and EGG parameters was undertaken using Student paired t tests. The reduced metallic singing mode Curbing has an identifiable laryngeal gesture. Curbing has a more open setting than Overdrive and Edge, with high visibility of the vocal folds, and the false folds giving a rectangular appearance. LTAS showed statistically significant differences between Curbing and the full metallic modes, with less energy across all spectra, yielding a high second and a low third harmonic. Statistically significant differences were identified on Max Qx, Average Qx, Shimmer+, Shimmer-, Shimmer dB, normalized noise energy, cepstral peak prominence, harmonics-to-noise ratio, and mean sound pressure level (P ≤ 0.05). Curbing as a voice production strategy is statistically significantly different from Overdrive and Edge, and can be categorized based on audio perception. This study demonstrates consistently different laryngeal gestures between Curbing and Overdrive and Edge, with high corresponding differences in LTAS, EGG and acoustic measures.