Vocal loading tasks are often used to investigate the relationship between voice use and vocal fatigue in laboratory settings. The present study investigated the concept of a novel quantitative dose-based vocal loading task for vocal fatigue evaluation. Ten female subjects participated in the study. Voice use was monitored and quantified using an online vocal distance dose calculator during six consecutive 30-min long sessions. Voice quality was evaluated subjectively using the CAPE-V and SAVRa before, between, and after each vocal loading task session. Fatigue-indicative symptoms, such as cough, swallowing, and voice clearance, were recorded. Statistical analysis of the results showed that the overall severity, the roughness, and the strain ratings obtained from CAPE-V obeyed similar trends as the three ratings from the SAVRa. These metrics increased over the first two thirds of the sessions to reach a maximum, and then decreased slightly near the session end. Quantitative metrics obtained from surface neck accelerometer signals were found to obey similar trends. The results consistently showed that an initial adjustment of voice quality was followed by vocal saturation, supporting the effectiveness of the proposed loading task. These tools require specific vocal stimuli. For example, the CAPE-V requires the completion of three defined phonation tasks assessed through perceptual rating. This therefore limits the applicability of these tools in situations where the vocal stimuli are varied or unspecified. Many studies have investigated uncertainties in subjective judgment methodologies for voice quality evaluation. Kreiman and Gerratt investigated the source of listener disagreement in voice quality assessment using unidimensional rating scales, and found that no single metric from natural voice recordings allowed the evaluation of voice quality [6]. Kreiman also found that individual standards of voice quality, scale resolution, and voice attribute magnitude also significantly influenced intra-rater agreement [7]. Objective metrics obtained using various acoustic instruments have been investigated, and attempts have been made to correlate these with perceptual voice quality assessments [8][9][10][11][12].A plethora of temporal, spectral, and cepstral metrics have been proposed to evaluate voice quality [13,14]. Commonly used features or vocal metrics include fundamental frequency ( f 0), loudness, jitter, shimmer, vocal formants, harmonic-to-noise ratio (HNR), spectral tilt (H1-H2, harmonic richness factor), maximum flow declination rate (MFDR), duty ratio, cepstral peak prominence (CPP), Mel-frequency cepstral coefficients (MFCCs), power spectrum ratio, and others [15][16][17][18][19]. Self-reported feelings of decreased vocal functionality have been used as a criterion for vocal fatigue in many previous studies [1,4,[20][21][22]. Standard self-administered questionnaires, such as the SAVRa and the Vocal Fatigue Index (VFI), have been used to identify individuals with vocal fatigue, and to characterize their sy...

The purpose of this study was to investigate the feasibility of using neck-surface acceleration signals to discriminate between modal, breathy and pressed voice. Voice data for five English single vowels were collected from 31 female native Canadian English speakers using a portable Neck Surface Accelerometer (NSA) and a condenser microphone. Firstly, auditory-perceptual ratings were conducted by five clinically-certificated Speech Language Pathologists (SLPs) to categorize voice type using the audio recordings. Intra- and inter-rater analyses were used to determine the SLPs’ reliability for the perceptual categorization task. Mixed-type samples were screened out, and congruent samples were kept for the subsequent classification task. Secondly, features such as spectral harmonics, jitter, shimmer and spectral entropy were extracted from the NSA data. Supervised learning algorithms were used to map feature vectors to voice type categories. A feature wrapper strategy was used to evaluate the contribution of each feature or feature combinations to the classification between different voice types. The results showed that the highest classification accuracy on a full set was 82.5%. The breathy voice classification accuracy was notably greater (approximately 12%) than those of the other two voice types. Shimmer and spectral entropy were the best correlated metrics for the classification accuracy.