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 present event-related brain potential (ERP) study investigates mechanisms underlying the processing of morphosyntactic information during real-time auditory sentence comprehension in French. Employing an auditory-visual sentence-picture matching paradigm, we investigated two types of anomalies using entirely grammatical auditory stimuli: (i) semantic mismatches between visually presented actions and spoken verbs, and (ii) number mismatches between visually presented agents and corresponding morphosyntactic number markers in the spoken sentences (determiners, pronouns in liaison contexts, and verb-final “inflection”). We varied the type and amount of number cues available in each sentence using two manipulations. First, we manipulated the verb type, by using verbs whose number cue was audible through subject (clitic) pronoun liaison (liaison verbs) as well as verbs whose number cue was audible on the verb ending (consonant-final verbs). Second, we manipulated the pre-verbal context: each sentence was preceded either by a neutral context providing no number cue, or by a subject noun phrase containing a subject number cue on the determiner. Twenty-two French-speaking adults participated in the experiment. While sentence judgment accuracy was high, participants' ERP responses were modulated by the type of mismatch encountered. Lexico-semantic mismatches on the verb elicited the expected N400 and additional negativities. Determiner number mismatches elicited early anterior negativities, N400s and P600s. Verb number mismatches elicited biphasic N400-P600 patterns. However, pronoun + verb liaison mismatches yielded this pattern only in the plural, while consonant-final changes did so in the singular and the plural. Furthermore, an additional sustained frontal negativity was observed in two of the four verb mismatch conditions: plural liaison and singular consonant-final forms. This study highlights the different contributions of number cues in oral language processing and is the first to investigate whether auditory-visual mismatches can elicit errors reminiscent of outright grammatical errors. Our results emphasize that neurocognitive mechanisms underlying number agreement in French are modulated by the type of cue that is used to identify auditory-visual mismatches.

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.

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