Diplophonia is a type of disordered voice in which two simultaneous pitches are perceived. Most commonly in diplophonic voices, the vocal folds are divided into two parts that vibrate at different frequencies. The glottal area is the projected area of the space between the vocal folds. The glottal area in time is referred to as the glottal area waveform (GAW). The GAW is modeled for diplophonic voice by superimposing two partial GAWs (pGAWs) that are trains of single-peak pulses with different pulse frequencies, i.e., fundamental frequencies ($f_o$s). In current kinematic models of diplophonic vocal fold vibration, the pGAWs are assumed to be quasiperiodic. This assumption is mitigated here by modulating pulse-to-pulse cycle length and amplitude. Both random and deterministic modulations are considered. Deterministic modulations depend on the difference of the pGAWs' instantaneous phases. Model GAWs are fitted to input GAWs using an analysis-by-synthesis approach which we refer to as `modulated pulse trains decomposition' (MPD). MPD is shown to be applicable to diplophonic as well as to nondiplophonic types of dysphonia, which include multi-pulse patterns, random timing behaviours, and chaos. It is mostly robust against modulations but degraded by large random modulations. MPD is compared to a deep autoencoder neural network, and the WaveGlow neural network. In terms of time-domain fitting errors, MPD outperforms the other two approaches unless random modulations are large. MPD outperforms the best of the other two approaches by up to approximately 5 dB. For large random modulations, the deep autoencoder network achieves the smallest fitting errors. In terms of magnitude spectrum fitting errors, WaveGlow is superior except for natural input GAWs containing only nondiplophonic types of dysphonia. Also pulse timing errors are shown to be advantageous for MPD.

Diplophonia is a type of pathological voice in which two fundamental frequencies (fo) are present simultaneously. Specialized audio analyzers that can handle up to two fos in diplophonic voices are in their infancy. We propose the tracking of up to two fos in diplophonic voices by audio waveform modeling (AWM), which involves obtaining candidates by repetitive execution of the Viterbi algorithm, followed by waveform Fourier synthesis, and heuristic candidate selection with majority voting. Our approach is evaluated with reference fo-tracks obtained from laryngeal highspeed videos of 29 sustained phonations and compared to state-of-the-art tracking algorithms for multiple fos. An accurate and a fast variant of our algorithm are tested. The median error rate of the accurate variant is 6.52%, whereas the most accurate benchmark achieves 11.11%. The fast variant is more than twice as fast as the fastest relevant benchmark, and the median error rate is 9.52%. Furthermore, illustrative results of connected speech analysis are reported. Our approach may help to improve detection and analysis of diplophonia in clinical research and practice, as well as to advance synthesis of disordered voices.

Determination of pitch marks (PMs) is necessary in clinical voice assessment for the measurement of fundamental frequency (F0) and perturbation. In voice with ambiguous F0, PM determination is crucial, and its validity needs special attention. The study at hand proposes a new approach for PM determination from Laryngeal High-Speed Videos (LHSVs), rather than from the audio signal. In this novel approach, double PMs are extracted from a diplophonic voice sample, in order to account for ambiguous F0s. The LHSVs are spectrally analyzed in order to extract dominant oscillation frequencies of the vocal folds. Unit pulse trains with these frequencies are created as PM trains and compensated for the phase shift. The PMs are compared to Praat's single audio PMs. It is shown that double PMs are needed in order to analyze diplophonic voice, because traditional single PMs do not explain its double-source characteristic.