Usefulness of Vocal Fatigue Index for Hypertension of Extrinsic Laryngeal Muscles

Background and Objectives This study compares Vocal Fatigue Index (VFI) scores according to the presence or absence of external laryngeal tension in hyperfunctional voice disorder. And through this, it is to confirm the usefulness of VFI to hypertension of extrinsic laryngeal muscles.Materials and Method The subjects were 61 female diagnosed with hyperfunctional voice disorder (hypertension group 41, non-hypertension group 20). The author palpated extrinsic laryngeal muscles for evaluation of hypertension and classified them as the presence or absence. The voice measurements were jitter, shimmer, Korean-Voice Handicap Index-10 (K-VHI-10), and Korean-Vocal Fatigue Index (K-VFI). The voice compared were according to the diagnosis and presence of hypertension only for patients with hyperfunctional voice disorder.Results As a result of comparing the voice measurement according to the presence or absence of hypertension, there was no significant difference in the acoustic variables, K-VHI-10 and K-VFI-Total, K-VFI-Fatigue. Whereas, K-VFI-Physical (p=0.006) and K-VFI-Rest (p=0.022) were significantly higher in the hypertension group.Conclusion These results indicate that the hypertension group has more physical discomfort and less voice recovery than the group without hypertension. It means that K-VFI can measure the physical discomfort and limitations of voice recovery due to hypertension of the external laryngeal muscle. The VFI can be used as one of the methods to evaluate the hypertension of the external laryngeal muscle in Hyperfunctional voice disorder.

Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

Our previous studies demonstrated that it is possible to perform the classification of both simulated pressed and actual vocally fatigued voice productions versus vocally healthy productions through the pattern recognition of sEMG signals obtained from subjects’ anterior neck. In these studies, the commonly accepted Vocal Fatigue Index factor 1 (VFI-1) was used for the ground-truth labeling of normal versus vocally fatigued voice productions. Through recent experiments, other factors with potential effects on classification were also studied, such as sEMG signal normalization, and data imbalance—i.e., the large difference between the number of vocally healthy subjects and of those with vocal fatigue. Therefore, in this paper, we present a much improved classification method derived from an extensive study of the effects of such extrinsic factors on the classification of vocal fatigue. The study was performed on a large number of sEMG signals from 88 vocally healthy and fatigued subjects including student teachers and teachers and it led to important conclusions on how to optimize a machine learning approach for the early detection of vocal fatigue.