Investigation of Vocal Bifurcations and Voice Patterns Induced by Asymmetry of Pathological Vocal Folds

Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection

Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of 10 common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate, Root-Mean-Square Energy, and Mel-frequency Cepstral Coefficients using a thousand voice signals from the Saarbruecken Voice Database dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors. The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.

Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices

OBJECTIVE

The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies.

METHODS

Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18-80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships.

RESULTS

Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68-0.95, with outliers; r2 = 0.80-0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors.

CONCLUSIONS

These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.

Formant-Aware Spectral Analysis of Sustained Vowels of Pathological Breathy Voice

OBJECTIVES

This paper reports the effectiveness of formant-aware spectral parameters to predict the perceptual breathiness rating. A breathy voice has a steeper spectral slope and higher turbulent noise than a normal voice. Measuring spectral parameters of acoustic signals over lower formant regions is a known approach to capture the properties related to breathiness. This study examines this approach by testing the contemporary spectral parameters and algorithms within the framework, alternate frequency band designs, and vowel effects.

METHODS

Sustained vowel recordings (/a/, /i/, and /u/) of speakers with voice disorders in the German Saarbrueken Voice Database were considered (n: 367). Recordings with signal irregularities, such as subharmonics or with roughness perception, were excluded from the study. Four speech language pathologists perceptually rated the recordings for breathiness on a 100-point scale, and their averages were used in the analysis. The acoustic spectra were segmented into four frequency bands according to the vowel formant structures. Five spectral parameters (intraband harmonics-to-noise ratio, HNR; interband harmonics ratio, HHR; interband noise ratio, NNR; and interband glottal-to-noise energy, GNE, ratio) were evaluated in each band to predict the perceptual breathiness rating. Four HNR algorithms were tested.

RESULTS

Multiple linear regression models of spectral parameters, led by the HNRs, were shown to explain up to 85% of the variance in perceptual breathiness ratings. This performance exceeded that of the acoustic breathiness index (82%). Individually, the HNR over the first two formants best explained the variances in the breathiness (78%), exceeding the smoothed cepstrum peak prominence (74%). The performance of HNR was highly algorithm dependent (10% spread). Some vowel effects were observed in the perceptual rating (higher for /u/), predictability (5% lower for /u/), and model parameter selections.

CONCLUSIONS

Strong per-vowel breathiness acoustic models were found by segmenting the spectrum to isolate the portion most affected by breathiness.