Nonlinear Dynamic Analysis and Perturbation Measurement Used for Discriminating Pathological Voices and Their Correlations With Perceptual Evaluation

OBJECTIVE

1. To investigate the discriminatory and diagnostic power of nonlinear dynamic analysis measures concerning voices from normal, benign, and malignant voice disorders. 2. To study the correlations of nonlinear dynamic analysis measures with perceptual ratings to evaluate the reliability of the objective acoustic analysis in predicting severity of voice.

METHOD

The perturbation analysis metrics used were Jitter%, Shimmer%, and signal-to-noise ratio. The nonlinear dynamic analysis metrics used were spectrum convergence ratio (SCR), nonlinear energy difference ratio (NEDR), and rate of divergence (ROD). Subjects were enrolled and divided into three groups based on laryngeal pathology: normal, benign, and malignant. Vowel sound and reading samples were recorded. Perceptual evaluation was applied to these voice samples to investigate correlations between metrics and auditory perception.

RESULT

Each metric was capable of discriminating laryngeal pathology, except for SCR in the case of distinguishing between benign and malignant pathologies. Perturbation analysis parameters had a moderate ability to differentiate between normal and benign pathologies, but were unable to characterize malignant pathologies for certain diseases, such as Reinke's edema. All metrics significantly correlated with perceptual G scores. Nonlinear dynamic analysis was superior when applied to cases of severe dysphonia, where linear metrics such as Jitter% and Shimmer% tended to lose utility. NEDR and ROD were successful at differentiating between the different pathologies, whereas SCR could not discriminate between the benign and malignant groups.

CONCLUSION

Perturbation and nonlinear dynamic analyses are comparable in their discriminating power with respect to normal and benign voices, and normal and malignant voices. The nonlinear dynamic analysis metrics NEDR and ROD may be superior in clinical settings with respect to discriminating voice pathology ranging from mild pathological voice to severe dysphonia, and with respect to discriminating benign and malignant voice. SCR was found unable to discriminate pathological voices.

The Influence of Voice Training on Vocal Learner's Objective Acoustic Voice Components

OBJECTIVE

Acoustic parameters of voice were studied in music majors throughout 18 months of training to understand the influence of voice training on voice.

METHODS

Twenty-three students from Xiamen Music School between 12 and 15 years old were enrolled. Acoustic examination was performed three times- every 6 months for 18 months. Various traditional acoustic parameters were measured, including dysphonia severity index (DSI), jitter, and D-value of vocal range. Nonlinear dynamic measures were also measured, including diffusive chaos to construct voice type component profiles (VTCPs), spectrum convergence ratio, and nonlinear energy difference ratio. The results were analyzed by multivariate analysis of variance.

RESULTS

Over the study duration, there was an improvement of DSI (P = 0.002), and D-value of vocal range (P = 0.000). Among nonlinear parameters, only voice type component data demonstrated significant changes during the study duration. Both Voice Type Component 1(VTC1) and VTC3 values differed from Time 1 to Time 2 as well as from Time 1 to Time 3. The proportion of VTC1 in samples generally decreased, while VTC3, representative of aperiodicity, increased. Both nonlinear energy difference ratio and spectrum convergence ratio exhibited no significant changes throughout the study.

CONCLUSION

Professional voice training can improve DSI and D-value of vocal range in singers' voices. These parameters have potential to be used for voice training evaluation and screening. Many nonlinear parameters did not detect differences in the healthy voices studied, but VTCPs created using intrinsic dimension present a valuable new method, visualizing increases in aperiodicity of the speaking voices after professional voice training.