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.

Mobile health wearables are often embedded with small processors for signal acquisition and analysis. These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. Herein, evolutionary algorithms are used to find novel DNNs that are accurate in classifying airway symptoms while allowing wearable deployment. As opposed to typical microphone‐acoustic signals, mechano‐acoustic data signals, which did not contain identifiable speech information for better privacy protection, are acquired from laboratory‐generated and publicly available datasets. The optimized DNNs had a low model file size of less than 150 kB and predicted airway symptoms of interest with 81.49% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel‐frequency bands up to 8,000 Hz are found as the most important feature for the classification. It is further found that DNN decisions are consistently relying on these specific features, fostering trust and transparency of the proposed DNNs. The proposed efficient and explainable DNN is expected to support edge computing on mechano‐acoustic sensing wearables for remote, long‐term monitoring of airway symptoms.

Discrimination between normal and pathological voice is a critical component in laryngeal pathology diagnosis and vocal rehabilitative treatment. In the present study, a portable miniature glottal notch accelerometer (GNA) device with supervised machine learning techniques was proposed to discriminate between three human voice types: normal, breathy, and pressed voice. Fourteen native American English speakers who were wearing a GNA device produced five different English single vowels in each of the three voice types. Acoustic features of the GNA signals were extracted using spectral analysis. Preliminary assessments of feature discrepancy among different voice types were made to present physical clues of discrimination. The linear discriminant analysis technique was applied to reduce the dimensionality of the raw-feature vector of the GNA signals. Maximization of between-class distance and minimization of within-class distance were synchronously achieved. The voice types were then classified using several supervised learning techniques, such as Linear Discriminant, Decision Tree, Support Vector Machine, and K-Nearest Neighbors. A classification accuracy of up to 91.0% was achieved. One mapping model from voice input to type output was eventually obtained based on the training set, so as to make predictions with new data in the future work.

Background Neck surface accelerometer (NSA) wearable devices have been developed for voice and upper airway health monitoring. As opposed to acoustic sounds, NSA senses mechanical vibrations propagated from the vocal tract to neck skin, which are indicative of a person’s voice and airway conditions. NSA signals do not carry identifiable speech information and a speaker’s privacy is thus protected, which is important and necessary for continuous wearable monitoring. Our device was already tested for its durable endurance and signal processing algorithms in controlled laboratory conditions. Objective This study aims to further evaluate both instrument and analysis validity in a group of occupational vocal users, namely, voice actors, who use their voices extensively at work in an ecologically valid setting. Methods A total of 16 professional voice actors (age range 21-50 years; 11 females and 5 males) participated in this study. All participants were mounted with an NSA on their sternal notches during the voice acting and voice assessment sessions. The voice acting session was 4-hour long, directed by a voice director in a professional sound studio. Voice assessment sessions were conducted before, during, and 48 hours after the acting session. The assessment included phonation tasks of passage reading, sustained vowels, maximum vowel phonation, and pitch glides. Clinical acoustic metrics (eg, fundamental frequency, cepstral measures) and a vocal dose measure (ie, accumulated distance dose from acting) were computed from NSA signals. A commonly used online questionnaire (Self-Administered Voice Rating questionnaire) was also implemented to track participants’ perception of vocal fatigue. Results The NSA wearables stayed in place for all participants despite active body movements during the acting. The ensued body noise did not interfere with the NSA signal quality. All planned acoustic metrics were successfully derived from NSA signals and their numerical values were comparable with literature data. For a 4-hour long voice acting, the averaged distance dose was about 8354 m with no gender differences. Participants perceived vocal fatigue as early as 2 hours after the start of voice acting, with recovery 24-48 hours after the acting session. Among all acoustic metrics across phonation tasks, cepstral peak prominence and spectral tilt from the passage reading most closely mirrored trends in perceived fatigue. Conclusions The ecological validity of an in-house NSA wearable was vetted in a workplace setting. One key application of this wearable is to prompt occupational voice users when their vocal safety limits are reached for duly protection. Signal processing algorithms can thus be further developed for near real-time estimation of clinically relevant metrics, such as accumulated distance dose, cepstral peak prominence, and spectral tilt. This functionality will enable continuous self-awareness of vocal behavior and protection of vocal safety in occupational voice users.