The Dysphonia Severity Index (DSI) is a measure that quantifies the overall vocal quality. The aim of the study is to evaluate the reliability of DSI measurements. The DSIs of 30 subjects were therefore measured using LingWAVES (WEVOSYS) and DiVAS (XION). To evaluate the inter-device reliability of DSI measurement, the devices' results were compared for each subject. The DSI values of both devices showed great differences. The calculated DSI differences of 95% of the subjects were within the limits of +2.39 and -2.82, which makes a clinical interpretation of severity of voice disorder using different devices questionable. The technical and procedural aspects of measurement divergences are discussed, and the need to define hardware and software standards is shown.

Diplophonia is a type of pathological voice in which two fundamental frequencies ( $f_o$) are present simultaneously. Specialized audio analyzers that can handle up to two $f_o$s in diplophonic voices are in their infancy. We propose the tracking of up to two $f_o$ s 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 $f_o$-tracks obtained from laryngeal high-speed videos of 29 sustained phonations and compared to state-of-the-art tracking algorithms for multiple $f_o$s. 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.

Objectives: The aims of this study are to investigate the effects of diplophonia on jitter and shimmer and to identify measurement limitations with regard to material selection and clinical interpretation. Materials and Methods: Four hundred and ninety-eight audio samples of sustained phonations were analyzed. The audio samples were assessed for the grade of hoarseness and the presence of diplophonia. Jitter and shimmer were reported with regard to perceptual ratings. We investigated cycle marker positions exemplarily and qualitatively to understand their implications for perturbation measurements. Results: Medians of jitter and shimmer were higher for diplophonic voices than for nondiplophonic voices with equal grades of hoarseness. The variance of jitter for moderately dysphonic voices was larger than the variance observed in a corpus from which diplophonic samples had been discarded. The positions of cycle markers in diplophonic voices did not match the positions of the pulses, indicating that the validity of jitter and shimmer values for these voices were questionable. Conclusion: Diplophonia biases the reporting of dysphonia severity via perturbation measures, and their validity is questionable for these voices. In addition, diplophonia is an influential source of variance in jitter measurements. Thus, diplophonic fragments of voice samples should be excluded prior to perturbation analysis.

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.