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This study compares the use of electroglottograms (EGGs) and glottal area waveforms (GAWs) to study phonation in different vibratory states as produced by professionally trained singers. Six western classical tenors were asked to phonate pitch glides from modal to falsetto phonation, or from modal to their stage voice above the passaggio (SVaP). For each pitch glide the sample entropy (SampEn) of the EGG signal was calculated to detect the occurrence of phonatory instabilities and establish a "ground truth" for the performed phonation type. The cycles before the maximum SampEn were labeled as modal, and the cycles after the peak were labeled as either falsetto, or SVaP. Three automatic categorizations of vibratory state were performed using clustering: one based only on the EGG, one based on the GAW, and one based on their combination. The error rate (clustering vs ground truth) was, on average, lower than 10% for all of the three settings, revealing no special advantage of the GAW over EGG, and vice versa. Modal voice cycles exhibited a larger contact quotient, larger normalized derivative peak ratio, and lower rise time, compared to SVaP and falsetto. The GAW-based normalized maximum area declination rate was larger in SVaP compared to modal voice. [ABSTRACT FROM AUTHOR]

The vocal folds can oscillate in several different ways, manifest to practitioners and clinicians as "registers" or "mechanisms," of which the two most often considered are modal voice and falsetto voice. Here these will be taken as instances of different "vibratory states," i.e., distinct quasi-stationary patterns of vibration of the vocal folds. State transitions are common in biomechanical nonlinear oscillators, and they are often abrupt and impossible to predict exactly. Therefore, vibratory states are a source of confounding variation, for instance when acquiring a voice range profile (VRP). In the quest for a state-based, non-invasive VRP, a semi-automatic method based on the short-term spectrum of the electroglottographic (EGG) signal was developed. The method identifies rapid vibratory state transitions, such as the modal-falsetto switch, and clusters the EGG data based on their similarities in the relative levels and phases of the lower frequency components. Productions of known modal and falsetto voice were accurately clustered by a Gaussian mixture model. When mapped into the VRP, this EGG-based clustering revealed connected regions of different vibratory sub-regimes in both modal and falsetto. [ABSTRACT FROM AUTHOR]