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Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we propose the concept of a novel lip-language decoding system with self-powered, low-cost, contact and flexible triboelectric sensors and a well-trained dilated recurrent neural network model based on prototype learning. The structural principle and electrical properties of the flexible sensors are measured and analysed. Lip motions for selected vowels, words, phrases, silent speech and voice speech are collected and compared. The prototype learning model reaches a test accuracy of 94.5% in training 20 classes with 100 samples each. The applications, such as identity recognition to unlock a gate, directional control of a toy car and lip-motion to speech conversion, work well and demonstrate great feasibility and potential. Our work presents a promising way to help people lacking a voice live a convenient life with barrier-free communication and boost their happiness, enriches the diversity of lip-language translation systems and will have potential value in many applications. Lip-language decoding systems are a promising technology to help people lacking a voice live a convenient life with barrier-free communication. Here, authors propose a concept of such system integrating self-powered triboelectric sensors and a well-trained dilated RNN model based on prototype learning.

The aim of this study was to recommend protocols for instrumental assessment of voice production in the areas of laryngeal endoscopic imaging, acoustic analyses, and aerodynamic procedures, which will (a) improve the evidence for voice assessment measures, (b) enable valid comparisons of assessment results within and across clients and facilities, and (c) facilitate the evaluation of treatment efficacy. Existing evidence was combined with expert consensus in areas with a lack of evidence. In addition, a survey of clinicians and a peer review of an initial version of the protocol via VoiceServe and the American Speech-Language-Hearing Association's Special Interest Group 3 (Voice and Voice Disorders) Community were used to create the recommendations for the final protocols. The protocols include recommendations regarding technical specifications for data acquisition, voice and speech tasks, analysis methods, and reporting of results for instrumental evaluation of voice production in the areas of laryngeal endoscopic imaging, acoustics, and aerodynamics. The recommended protocols for instrumental assessment of voice using laryngeal endoscopic imaging, acoustic, and aerodynamic methods will enable clinicians and researchers to collect a uniform set of valid and reliable measures that can be compared across assessments, clients, and facilities.

Objectives To evaluate the reliability and validity of the Artificial Intelligence Performance Instrument (AIPI). Methods Medical records of patients consulting in otolaryngology were evaluated by physicians and ChatGPT for differential diagnosis, management, and treatment. The ChatGPT performance was rated twice using AIPI within a 7-day period to assess test–retest reliability. Internal consistency was evaluated using Cronbach’s α . Internal validity was evaluated by comparing the AIPI scores of the clinical cases rated by ChatGPT and 2 blinded practitioners. Convergent validity was measured by comparing the AIPI score with a modified version of the Ottawa Clinical Assessment Tool (OCAT). Interrater reliability was assessed using Kendall’s tau. Results Forty-five patients completed the evaluations (28 females). The AIPI Cronbach’s alpha analysis suggested an adequate internal consistency ( α  = 0.754). The test–retest reliability was moderate-to-strong for items and the total score of AIPI ( r s  = 0.486, p  = 0.001). The mean AIPI score of the senior otolaryngologist was significantly higher compared to the score of ChatGPT, supporting adequate internal validity ( p  = 0.001). Convergent validity reported a moderate and significant correlation between AIPI and modified OCAT ( r s  = 0.319; p  = 0.044). The interrater reliability reported significant positive concordance between both otolaryngologists for the patient feature, diagnostic, additional examination, and treatment subscores as well as for the AIPI total score. Conclusions AIPI is a valid and reliable instrument in assessing the performance of ChatGPT in ear, nose and throat conditions. Future studies are needed to investigate the usefulness of AIPI in medicine and surgery, and to evaluate the psychometric properties in these fields.

Purpose: The purpose of this document is threefold: (a) review the uses of the terms \"vocal fatigue,\" \"vocal effort,\" \"vocal load,\" and \"vocal loading\" (as found in the literature) in order to track the occurrence and the related evolution of research; (b) present a \"linguistically modeled\" definition of the same from the review of literature on the terms; and (c) propose conceptualized definitions of the concepts. Method: A comprehensive literature search was conducted using PubMed/MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scientific Electronic Library Online. Four terms (\"vocal fatigue,\" \"vocal effort,\" \"vocal load,\" and \"vocal loading\"), as well as possible variants, were included in the search, and their usages were compiled into conceptual definitions. Finally, a focus group of eight experts in the field (current authors) worked together to make conceptual connections and proposed consensus definitions. Results: The occurrence and frequency of \"vocal load,\" \"vocal loading,\" \"vocal effort,\" and \"vocal fatigue\" in the literature are presented, and summary definitions are developed. The results indicate that these terms appear to be often interchanged with blurred distinctions. Therefore, the focus group proposes the use of two new terms, \"vocal demand\" and \"vocal demand response,\" in place of the terms \"vocal load\" and \"vocal loading.\" We also propose standardized definitions for all four concepts. Conclusion: Through a comprehensive literature search, the terms \"vocal fatigue,\" \"vocal effort,\" \"vocal load,\" and \"vocal loading\" were explored, new terms were proposed, and standardized definitions were presented. Future work should refine these proposed definitions as research continues to address vocal health concerns.

Objective To describe and visually depict laryngeal complications in patients recovering from coronavirus disease 2019 (COVID‐19) infection along with associated patient characteristics. Study design Prospective patient series. Setting Tertiary laryngology care centers. Subjects and methods Twenty consecutive patients aged 18 years or older presenting with laryngological complaints following recent COVID‐19 infection were included. Patient demographics, comorbid medical conditions, COVID‐19 diagnosis dates, symptoms, intubation, and tracheostomy status, along with subsequent laryngological symptoms related to voice, airway, and swallowing were collected. Findings on laryngoscopy and stroboscopy were included, if performed. Results Of the 20 patients enrolled, 65% had been intubated for an average duration of 21.8 days and 69.2% requiring prone‐position mechanical ventilation. Voice‐related complaints were the most common presenting symptom, followed by those related to swallowing and breathing. All patients who underwent flexible laryngoscopy demonstrated laryngeal abnormalities, most frequently in the glottis (93.8%), and those who underwent stroboscopy had abnormalities in mucosal wave (87.5%), periodicity (75%), closure (50%), and symmetry (50%). Unilateral vocal fold immobility was the most common diagnosis (40%), along with posterior glottic (15%) and subglottic (10%) stenoses. 45% of patients underwent further procedural intervention in the operating room or office. Many findings were suggestive of intubation‐related injury. Conclusion Prolonged intubation with prone‐positioning commonly employed in COVID‐19 respiratory failure can lead to significant laryngeal complications with associated difficulties in voice, airway, and swallowing. The high percentage of glottic injuries underscores the importance of stroboscopic examination. Otolaryngologists must be prepared to manage these complications in patients recovering from COVID‐19. Level of evidence IV.

Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

Purpose: High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold oscillations by segmenting the glottal area. This challenging task has been tackled by various studies; however, the proposed approaches are mostly limited and not suitable for daily clinical routine. Method: We developed a user-friendly software in C# that allows the editing, motion correction, segmentation, and quantitative analysis of HSV data. We further provide pretrained deep neural networks for fully automatic glottis segmentation. Results: We freely provide our software Glottis Analysis Tools (GAT). Using GAT, we provide a general threshold-based region growing platform that enables the user to analyze data from various sources, such as in vivo recordings, ex vivo recordings, and high-speed footage of artificial vocal folds. Additionally, especially for in vivo recordings, we provide three robust neural networks at various speed and quality settings to allow a fully automatic glottis segmentation needed for application by untrained personnel. GAT further evaluates video and audio data in parallel and is able to extract various features from the video data, among others the glottal area waveform, that is, the changing glottal area over time. In total, GAT provides 79 unique quantitative analysis parameters for video- and audio-based signals. Many of these parameters have already been shown to reflect voice disorders, highlighting the clinical importance and usefulness of the GAT software. Conclusion: GAT is a unique tool to process HSV and audio data to determine quantitative, clinically relevant parameters for research, diagnosis, and treatment of laryngeal disorders.

Introduction To update the European guidelines for the assessment of voice quality (VQ) in clinical practice. Methods Nineteen laryngologists–phoniatricians of the European Laryngological Society (ELS) and the Union of the European Phoniatricians (UEP) participated to a modified Delphi process to propose statements about subjective and objective VQ assessments. Two anonymized voting rounds determined a consensus statement to be acceptable when 80% of experts agreed with a rating of at least 3/4. The statements with ≥ 3/4 score by 60–80% of experts were improved and resubmitted to voting until they were validated or rejected. Results Of the 90 initial statements, 51 were validated after two voting rounds. A multidimensional set of minimal VQ evaluations was proposed and included: baseline VQ anamnesis (e.g., allergy, medical and surgical history, medication, addiction, singing practice, job, and posture), videolaryngostroboscopy (mucosal wave symmetry, amplitude, morphology, and movements), patient-reported VQ assessment (30- or 10-voice handicap index), perception (Grade, Roughness, Breathiness, Asthenia, and Strain), aerodynamics (maximum phonation time), acoustics (Mean F0, Jitter, Shimmer, and noise-to-harmonic ratio), and clinical instruments associated with voice comorbidities (reflux symptom score, reflux sign assessment, eating-assessment tool-10, and dysphagia handicap index). For perception, aerodynamics and acoustics, experts provided guidelines for the methods of measurement. Some additional VQ evaluations are proposed for voice professionals or patients with some laryngeal diseases. Conclusion The ELS-UEP consensus for VQ assessment provides clinical statements for the baseline and pre- to post-treatment evaluations of VQ and to improve collaborative research by adopting common and validated VQ evaluation approach.

Cleft palate is among the most common birth abnormalities. The success of primary surgery in the early months of life is crucial for successful feeding, hearing, dental development, and facial growth. Over recent decades, age at palatal surgery in infancy has reduced. The Timing Of Primary Surgery for cleft palate (TOPS) trial aims to determine whether, in infants with cleft palate, it is better to perform primary surgery at age 6 or 12months (corrected for gestational age).The TOPS trial is an international, two-arm, parallel group, randomised controlled trial. The primary outcome is insufficient velopharyngeal function at 5years of age. Secondary outcomes, measured at 12months, 3years, and 5years of age, include measures ofspeech development, safety of the procedure, hearing level, middle ear function, dentofacial development, and growth. The analysis approaches for primary and secondary outcomes are described here, as are the descriptive statistics which will be reported. The TOPS protocol has been published previously.This paper provides details of the planned statistical analyses for the TOPS trial and will reduce the risk of outcome reporting bias and data-driven results.ClinicalTrials.gov NCT00993551 . Registered on 9 October 2009.

Purpose: Thickened liquids are frequently used as an intervention for dysphagia, but gaps persist in our understanding of variations in swallowing behavior based on incremental thickening of liquids. The goal of this study was to establish reference values for measures of bolus flow and swallowing physiology in healthy adults across the continuum from thin to extremely thick liquids. Method: A sex-balanced sample of 38 healthy adults underwent videofluoroscopy and swallowed 20% weight-to-volume concentration barium prepared in thin and slightly, mildly, moderately, and extremely thick consistencies using a xanthan gum thickener. Participants took comfortable sips and swallowed without a cue; sip volume was measured based on presip and postsip cup weights. A standard operating procedure (the ASPEKT method: Analysis of Swallowing Physiology: Events, Kinematics and Timing) was used to analyze videofluoroscopy recordings. Results: The results clarify that, for thin liquid sips (10-14 ml), a single swallow without clearing swallows is typical and is characterized by complete laryngeal vestibule closure, complete pharyngeal constriction, and minimal postswallow residue. Aspiration was not seen, and penetration was extremely rare. Bolus position at swallow onset was variable, extending as low as the pyriform sinuses in 37% of cases. With thicker liquids, no changes in event sequencing, laryngeal vestibule closure, pharyngeal constriction, or postswallow residue were seen. The odds of penetration were significantly reduced. A longer timing interval until onset of the hyoid burst movement was seen, with an associated higher bolus position at swallow onset. Other timing measures remained unaffected by changes in bolus consistency. Conclusion: The results include new reference data for swallowing in healthy adults across the range from thin to extremely thick liquids.