METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices.
RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology.
CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.
METHODS: This was a prospective cohort study. Each patient underwent pre- and post-shift voice analysis.
RESULTS: Among 42 teleoperators, 28 patients (66.7 per cent) completed all the tests. Female predominance (62 per cent) was noted, with a mean age of 40 years. Voice changes during working were reported by 48.1 per cent. Pre- and post-shift maximum phonation time (p < 0.018) and Voice Handicap Index-10 (p < 0.011) showed significant results with no correlation noted between subjective and objective assessment.
CONCLUSION: Maximum phonation time and Voice Handicap Index-10 are good voice assessment tools. The quality of evidence is inadequate to recommend 'gold standard' voice assessment until a better-quality study has been completed.
DESIGN: Cross sectional reliability study.
SETTING: University teaching hospital.
METHODS: Fifty healthy volunteers and 50 voice disorder patients had supervised recordings in a quiet room using OperaVOX by the iPod's internal microphone with sampling rate of 45 kHz. A five-seconds recording of vowel/a/was used to measure fundamental frequency (F0), jitter, shimmer and noise-to-harmonic ratio (NHR). All healthy volunteers and 21 patients had a second recording. The recorded voices were also analysed using the MDVP. The inter- and intrasoftware reliability was analysed using intraclass correlation (ICC) test and Bland-Altman (BA) method. Mann-Whitney test was used to compare the acoustic parameters between healthy volunteers and patients.
RESULTS: Nine of 50 patients had severe aperiodic voice. The ICC was high with a confidence interval of >0.75 for the inter- and intrasoftware reliability except for the NHR. For the intersoftware BA analysis, excluding the severe aperiodic voice data sets, the bias (95% LOA) of F0, jitter, shimmer and NHR was 0.81 (11.32, -9.71); -0.13 (1.26, -1.52); -0.52 (1.68, -2.72); and 0.08 (0.27, -0.10). For the intrasoftware reliability, it was -1.48 (18.43, -21.39); 0.05 (1.31, -1.21); -0.01 (2.87, -2.89); and 0.005 (0.20, -0.18), respectively. Normative data from the healthy volunteers were obtained. There was a significant difference in all acoustic parameters between volunteers and patients measured by the Opera-VOX (P
MATERIALS AND METHODS: This cross-sectional study was carried out in the Otorhinolaryngology, Head and Neck Surgery Department of Universiti Kebangsaan Malaysia Medical Centre (UKMMC) from June 2015 to May 2016. The mVHI-10 was produced following a rigorous forward and backward translation. One hundred participants, including 50 healthy volunteers (17 male, 33 female) and 50 patients with voice disorders (26 male, 24 female), were recruited to complete the mVHI-10 before flexible laryngoscopic examinations and acoustic analysis. The mVHI-10 was repeated in 2 weeks via telephone interview or clinic visit. Its reliability and validity were assessed using interclass correlation.
RESULTS: The test-retest reliability for total mVHI-10 and each item score was high, with the Cronbach alpha of >0.90. The total mVHI-10 score and domain scores were significantly higher (P
METHODS: This prospective observational study comprised 34 newly diagnosed unilateral vocal fold paralysis patients undergoing surgical interventions: injection laryngoplasty or medialisation thyroplasty. Voice assessments, including maximum vocal intensity and other acoustic parameters, were performed at baseline and at one and three months post-intervention. Maximum vocal intensity was also repeated within two weeks before any surgical interventions were performed. The results were compared between different time points and between the two intervention groups.
RESULTS: Maximum vocal intensity showed high internal consistency. Statistically significant improvements were seen in maximum vocal intensity, Voice Handicap Index-10 and other acoustic analyses at one and three months post-intervention. A significant moderate negative correlation was demonstrated between maximum vocal intensity and Voice Handicap Index-10, shimmer and jitter. There were no significant differences in voice outcomes between injection laryngoplasty and medialisation thyroplasty patients at any time point.
CONCLUSION: Maximum vocal intensity can be applied as a treatment outcome measure in unilateral vocal fold paralysis patients; it can demonstrate the effectiveness of treatment and moderately correlates with self-reported outcome measures.
METHODS: This study was divided into two phases. Phase I tested the reliability of the Malay-VHI-10 while Phase II was a cross-sectional study with two-stage sampling. In Phase II, a self-administered questionnaire was used to collect socio-demographic and teaching characteristics, depression, anxiety and stress scale (Malay version of DASS-21); and health-related quality of life (Malay version of SF12-v2). Complex sample analysis was conducted using multivariate Poisson regression with robust variance.
RESULTS: In Phase I, the Spearman correlation coefficient and Cronbach alpha for total VHI-10 score was 0.72 (p < 0.001) and 0.77 respectively; showing good correlation and internal consistency. The ICCs ranged from 0.65 to 0.78 showing fair to good reliability and demonstrating the subscales to be reliable and stable. A total of 6039 teachers participated in Phase II. They were primarily Malays, females, married, had completed tertiary education and aged between 30 to 50 years. A total of 10.4% (95% CI 7.1, 14.9) of the teachers had voice disorder (VHI-10 score > 11). Compared to Malays, a greater proportion of ethnic Chinese teachers reported voice disorder while ethnic Indian teachers were less likely to report this problem. There was a higher prevalence ratio (PR) of voice disorder among single or divorced/widowed teachers. Teachers with voice disorder were more likely to report higher rates of absenteeism (PR: 1.70, 95% CI 1.33, 2.19), lower quality of life with lower SF12-v2 physical (0.98, 95% CI 0.96, 0.99) and mental (0.97, 95% CI 0.96, 0.98) component summary scales; and higher anxiety levels (1.04, 95% CI 1.02, 1.06).
CONCLUSIONS: The Malay-VHI-10 is valid and reliable. Voice disorder was associated with increased absenteeism, marginally associated with reduced health-related quality of life as well as increased anxiety among teachers.
METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.
RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.
DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.