Optimized Nonlinear Dynamic Analysis of Pathologic Voices With Laryngeal Paralysis Based on the Minimum Embedding Dimension

Differences and Reliability of Linear and Nonlinear Acoustic Measures as a Function of Vocal Intensity in Individuals With Voice Disorders

PURPOSE

Linear acoustic indices are significantly influenced by speaking voice intensity. The main aim of this work was to compare acoustic measures based on linear and nonlinear models in different speaking voice intensity levels and to analyze the reliability of those measures in different intensity levels in subjects with voice disorders.

METHODS

435 samples from subjects (314 women, 121 men with a mean age of 41.07 ± 13.73) diagnosed with various voice disorders were used. In total, 17 acoustic measures were derived from the vowel /ɛ/ sustained at three intensity levels (soft, comfortable, and loud). Five were linear (standard deviation of the fundamental frequency (f0), jitter, shimmer, harmonics-to-noise ratio (HNR) and smoothed cepstral peak prominence (CPPS)), and twelve were nonlinear measures, namely correlation dimension (D2), correlation entropy (H2), first minimum of the mutual information function (FMMI), relative entropy (ENTR-R), largest Lyapunov exponent (Lyap), determinism (DET), transitivity, mean diagonal line length (Lmed), Shannon entropy (ENTR-S), mean length of vertical structures, also known as trapping time (TT), laminarity (LAM) and recurrence period density entropy (RPDE). Differences between speaking voice intensity levels were assessed by Friedman's test and Nemenyi as posthoc test. Intraclass correlation coefficient was used to investigate if each acoustic measure remains in agreement (reliability) between different voice intensity levels.

RESULTS

There were significant differences in all acoustic measures about vocal intensity level (P < 0.001). Intraclass correlation coefficient was very good for HNR (>0.61) and good for Lyap, DET, ENTR-S, Lmed, RPDE, and TT (0.41-0.60).

CONCLUSIONS

All acoustic measures varied as a function of vocal intensity in voice disordered adults, while this relation was different for linear and nonlinear measures. Only the measures HNR, Lyap, DET, ENTR-S, Lmed, RPDE and TT had an acceptable reliability between different voice intensity levels. Therefore, patient`s voice SPL should be controlled or indicated during acoustic vocal assessment.

Pathological Voice Detection Based on Phase Reconstitution and Convolutional Neural Network

The nonlinear dynamic features can effectively describe the acoustic characteristics of normal and pathological voice. In this paper, the phase space reconstruction and convolution neural network are used to classify the normal and pathological voice. The phase space information of normal and pathological voice is reconstructed using delay time and embedding dimension, the one-dimensional signal is converted to a two-dimensional matrix, and the reconstructed trajectory graph sample of the signal is generated. The trajectory graph samples are used as the input of the VGG-like convolutional neural network, and the graphical features are extracted to achieve a classification of normal and pathological voice. In order to overcome the lack of clinical data, a data enhancement scheme is used. The experiment which classifies the normal and pathological voice is carried out on three pathological databases respectively, i.e. the Massachusetts eye and ear infirmary (MEEI) database, Saarbrücken voice database (SVD) database, and a clinical database collected by the authors. Five-fold cross validation is used and the average recognition rates on the three databases are 99.42%, 97.30% and 95.88% respectively. The average recognition rates are 96.04% and 92.27% for normal, vocal fold paralysis and vocal fold non-paralysis voice in MEEI database and SVD database. The experimental results show that the method has high classification recognition rate and good robustness, and has certain universal applicability for the recognition of the normal and pathological voice.

Descriptive Analysis of the Interactive Patterning of the Vocalization Subsystems in Healthy Participants: A Dynamic Systems Perspective

Purpose Normative data for many objective voice measures are routinely used in clinical voice assessment; however, normative data reflect vocal output, but not vocalization process. The underlying physiologic processes of healthy phonation have been shown to be nonlinear and thus are likely different across individuals. Dynamic systems theory postulates that performance behaviors emerge from the nonlinear interplay of multiple physiologic components and that certain patterns are preferred and loosely governed by the interactions of physiology, task, and environment. The purpose of this study was to descriptively characterize the interactive nature of the vocalization subsystem triad in subjects with healthy voices and to determine if differing subgroups could be delineated to better understand how healthy voicing is physiologically generated. Method Respiratory kinematic, aerodynamic, and acoustic formant data were obtained from 29 individuals with healthy voices (21 female and eight male). Multivariate analyses were used to descriptively characterize the interactions among the subsystems that contributed to healthy voicing. Results Group data revealed representative measures of the 3 subsystems to be generally within the boundaries of established normative data. Despite this, 3 distinct clusters were delineated that represented 3 subgroups of individuals with differing subsystem patterning. Seven of the 9 measured variables in this study were found to be significantly different across at least 1 of the 3 subgroups indicating differing physiologic processes across individuals. Conclusion Vocal output in healthy individuals appears to be generated by distinct and preferred physiologic processes that were represented by 3 subgroups indicating that the process of vocalization is different among individuals, but not entirely idiosyncratic. Possibilities for these differences are explored using the framework of dynamic systems theory and the dynamics of emergent behaviors. A revised physiologic model of phonation that accounts for differences within and among the vocalization subsystems is described. Supplemental Material https://doi.org/10.23641/asha.7616462.

Pathological Voice Source Analysis System Using a Flow Waveform-Matched Biomechanical Model

Voice production occurs through vocal cord and vibration coupled to glottal airflow. Vocal cord lesions affect the vocal system and lead to voice disorders. In this paper, a pathological voice source analysis system is designed. This study integrates nonlinear dynamics with an optimized asymmetric two-mass model to explore nonlinear characteristics of vocal cord vibration, and changes in acoustic parameters, such as fundamental frequency, caused by distinct subglottal pressure and varying degrees of vocal cord paralysis are analyzed. Various samples of sustained vowel /a/ of normal and pathological voices were extracted from MEEI (Massachusetts Eye and Ear Infirmary) database. A fitting procedure combining genetic particle swarm optimization and a quasi-Newton method was developed to optimize the biomechanical model parameters and match the targeted voice source. Experimental results validate the applicability of the proposed model to reproduce vocal cord vibration with high accuracy, and show that paralyzed vocal cord increases the model coupling stiffness.