Search Results (76)

Background and Objectives: Laryngostroboscopy is considered the gold standard for the functional assessment of vocal fold vibration and enables the detection of subtle structural and vibratory abnormalities that may not be apparent during routine examination. In interdisciplinary research involving speech analysis and prosthetic rehabilitation, exclusion of underlying laryngeal pathology is methodologically important. The aim of the present study was to evaluate the diagnostic findings obtained through laryngostroboscopic screening in asymptomatic Bulgarian adults examined within a broader research project on speech function and prosthetic rehabilitation. Materials and Methods: A prospective observational study was conducted between April 2022 and July 2023 at the Medical University–Varna, Bulgaria. Eighty adults without self-reported voice-related symptoms underwent laryngostroboscopic examination using an ATMOS Strobo 21 LED system (Advanced Technology Medical Systems GmbH, Lenzkirch, Germany). Participants were assessed for structural and functional laryngeal abnormalities, including alterations in movement frequency, oscillation amplitude, phase symmetry, and visible pathological changes. Descriptive statistics and chi-square tests and Fisher’s exact test analyses were used to evaluate possible associations between laryngeal pathology and demographic variables. Results: Normal laryngeal status was observed in 64 participants (80.0%), whereas 16 (20.0%) showed laryngostroboscopic findings. Isolated vibratory deviations were recorded separately and were not automatically classified as laryngeal pathology. Minor structural or functional variations were found in 5 participants (6.3%), functional laryngeal disorders in 6 (7.5%), benign lesions in 1 (1.3%), and diffuse inflammatory changes consistent with laryngitis in 4 (5.0%). Deviations in vibratory parameters were identified in 25 participants (31.3%) for movement frequency, 16 (20.0%) for oscillation amplitude, and 22 (27.5%) for phase synchronization. No statistically significant associations were found between laryngeal pathology and gender or age group (p > 0.05). Conclusions: Laryngostroboscopic examination identified structural and functional laryngeal findings in a proportion of asymptomatic adults recruited within a speech-function research framework. Functional vibratory deviations were observed more frequently than overt structural pathology. These findings demonstrate that previously unrecognized laryngeal abnormalities may be present even in individuals without apparent voice-related complaints. Further studies incorporating speech-function outcomes and larger cohorts are required to clarify the clinical significance of these observations. Full article

Cancer patients receiving systemic therapy experience substantial treatment-related symptoms. Nurse-led digital health interventions, e.g., interactive voice response systems, web platforms, mobile apps, and telehealth, have emerged as strategies to strengthen supportive care. To evaluate its effectiveness, this systematic review summarizes evidence exclusively from randomized controlled trials (RCTs). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, four databases were searched from inception to January 2025 for eligible RCTs involving adults undergoing anticancer therapy; evaluating nurse-led or nurse-co-led interventions using digital or telecommunication technologies; reporting validated symptom or health-related quality of life (HRQoL) outcomes. Risk of bias was assessed. Nine RCTs (N = 3344) met criteria; seven had low risk of bias. Interventions using telephone systems, web portals, mobile apps, or videoconferencing reduced symptom burden and improved HRQoL. The Symptom Care at Home system reduced symptom burden by ~43%, with greatest effects from combined automated monitoring and nurse practitioner follow-up. Additional benefits included improved anxiety, self-efficacy, patient participation, fewer severe toxicities and hospitalization days. In conclusion, nurse-led digital interventions effectively reduce symptom burden and support HRQoL during systemic therapy. Multicomponent models integrating automated monitoring with structured nursing follow-up and decision support appear most beneficial. Full article

Objectives: This study proposes an implementation-oriented design framework for multimodal conversational agents handling patient-generated health data and reports an exploratory experiment evaluating its instantiation in hypertension self-monitoring, focusing on user experience of conversational data-entry workflows. Methods: The framework operationalizes four complementary dimensions (social intelligence, communication style, anthropomorphic characteristics, and technological mapping) and was instantiated in two agents integrated into an eHealth platform. Each agent supports users by providing prompts, interpreting responses, checking data plausibility, and confirming submission. A three-arm, single-session feasibility experiment ($n=18$, $n=6$ per group) compared a conventional app interface with text-based and voice-based conversational agents. Evaluation triangulated three sources of evidence: open-ended qualitative responses analyzed through descriptive content analysis, session-level researcher observation notes, and the User Experience Questionnaire (UEQ) reported descriptively with one-way ANOVA and ${\eta }^2$ effect sizes. Results: All three modalities were acceptable to participants and produced UEQ scores in the positive range. Hesitation was observed in 2 of 6 Control participants, 1 of 6 Text participants, and 3 of 6 Voice participants, with self-reports indicating that voice-related difficulties were modality-specific (diction, command phrasing) and resolved within the session. Qualitative themes of acceptability and innovation, perceived effort, and modality-specific facilitators emerged across the corpus. Between-group ANOVAs did not reach statistical significance ($p>0.05$), as expected for an underpowered design, yet ${\eta }^2$ values were medium for Attractiveness, Efficiency, Dependability, and Pragmatic Quality and large for Stimulation and Hedonic Quality, converging with the qualitative innovation and engagement signal in the conversational conditions. Conclusions: The framework and feasibility experiment provide preliminary, hypothesis-generating evidence on the potential of multimodal conversational interfaces in healthcare. However, no clinical, behavioral, or longitudinal outcomes were assessed. The four design dimensions can be tentatively associated with themes recognizable in user discourse, and the observed effect-size pattern motivates adequately powered longitudinal studies that incorporate behavioral and clinical endpoints alongside user experience measures. Full article

Healthcare professionals must acquire and maintain both declarative knowledge and fine psychomotor skills across a wide range of clinical procedures. Human working memory is physiologically limited, and the high cognitive demands of clinical environments frequently contribute to medical errors and adverse events. Intra-individual performance variability—driven by fatigue, stress, and motivation—represents a further challenge that conventional medical safety education has not adequately addressed. According to the World Health Organization, patient harm ranks fourteenth in the global burden of disease, with approximately 10% of hospitalised patients in high-income countries experiencing harm within healthcare facilities. This study reports the design, theoretical rationale, and preliminary outcomes of an augmented reality (AR) glasses system for hands-free, self-directed medical procedural training, developed from a human factors and ergonomics (HFE) perspective. The system integrates a see-through head-mounted display (HMD; Epson Moverio BT-40S), bone-conduction earphones (Shokz OpenComm), and an industrial-grade voice recognition application (NEC Solution Innovators), achieving fully hands-free operation compatible with aseptic technique. Content design is grounded in cognitive load theory (CLT) and the cognitive theory of multimedia learning (CTML), extended by neuroscientific evidence on multisensory integration and memory consolidation. More than 40 procedure-specific modules have been developed in-house at Tokyo University of Technology, spanning airway management, vascular access, respiratory therapy, dialysis, and cardiac support. In a four-year longitudinal survey (virtual reality (VR) simulator; n = 286), major satisfaction items consistently exceeded the scale midpoint. In an AR endotracheal suctioning cohort (n = 38/22), procedural flow understanding was rated 3.95/5.0. A peer-reviewed randomised controlled trial (Clinical Simulation in Nursing, n = 36) demonstrated significantly superior skill improvement (p < 0.001) and learning motivation (p = 0.001) in the AR group versus textbook self-practice. Principal ergonomic limitations of current HMD hardware—excessive weight, narrow field of view, and absence of medical-grade certification—are documented, and AI-based real-time procedural assessment is identified as a priority for the next research phase. Full article

This systematic literature review (SLR) examines the evolving nature of blended learning (BL) in higher education from 2020 to 2025. While BL is widely promoted for its flexibility, accessibility, and inclusivity, its practical application is uneven. This review integrated findings from 63 peer-reviewed studies obtained from five major academic databases and employed the PRISMA 2020 protocol to ensure methodological transparency and research quality. Findings were analysed using the Technological Pedagogical Content Knowledge (TPACK) model and the AI Roles Framework to assess instructional design and technology integration. This analysis was conducted through a thematic approach to identify recurring patterns and key insights across the literature on BL and the use of emerging technologies such as AI, AR/VR, and learning analytics within BL. The study identifies three main challenges. There is an excessive application of tool-centric models such as TAM and UTAUT. Engagement with educators, administrators, and under-represented learners is limited. Ethical considerations related to the use of AI, AR/VR, and learning analytics are often overlooked. The most common issue is the higher dependence on models such as TAM and UTAUT. The review also shows that many educational applications reproduce similar design patterns and content structures, while insufficiently responding to learner diversity, digital readiness, accessibility needs, self-regulation capacity, and disciplinary context. While these models are helpful, they frequently limit complex teaching and learning environments to simple patterns of user behaviour. It is also clear that voices from teachers, support staff, and less-represented student groups are missing from the discussion. This is considered a serious concern because these technologies are increasingly being used in education. Moving forward, there is a clear need to shift away from rigid, technology-led models toward more adaptive, pedagogy-focused approaches. These findings contribute to a deeper understanding of blended learning as an interconnected educational system. They also offer implications for future research, inclusive digital pedagogy, and policy development in higher education. Full article

Objective: The aim of the study was to devise and validate a new questionnaire—Psycho-Vox—for diagnosis of muscle tension dysphonia and develop normative data for Polish-speaking adults. The tool was designed to measure 10 key psychological and social dimensions that have a bearing on functional voice disorders (FVDs), namely perseverance, coping with stress style (task-oriented or avoiding-oriented), mental well-being, resilience, burnout, family and social relationships, and tendency to project a positive self-image, tendency to exaggerate symptoms. Methods: The validation study involved 164 participants (46.3% were patients with muscle tension dysphonia and 53.7% were in good health; 92.7% were women). Their ages ranged from 18 to 76 years (M = 40.2; SD = 13.1). The Psycho-Vox questionnaire comprised 80 items, which participants rated on a 5-point Likert scale. Construct validity was tested using factor analysis with principal component rotation and confirmatory factor analysis. Reliability was determined using Cronbach’s α, and linguistic comprehension was assessed using the Flesch Reading Ease Score (FRES, Polish version) and the Jasnopis indicator. Sten norms were developed for the entire sample. Results: Factor analysis confirmed the 10-factor structure of the questionnaire, consistent with theoretical assumptions (KMO = 0.734; χ²(3160) = 8643.41; p < 0.001), with 89.2% classification agreement. Cronbach’s α for individual scales ranged from 0.705 to 0.837, confirming high internal reliability. The results of the FRES and Jasnopis tests indicate that the tool is understandable to adults with secondary or higher education. Significant differences were found between the patient group and the control group in terms of family relationships (p < 0.001), social capital (p = 0.003), mental well-being (p = 0.007), tendency to project a positive self-image (p = 0.004), and tendency to exaggerate symptoms (p = 0.005). A logistic regression model (χ²(10) = 62.27; p < 0.001; Hosmer–Lemeshow χ²(8) = 5.00; p = 0.757) showed that belonging to the patient group was positively associated with higher scores on the scales of family relationships, mental resilience, mental well-being, tendency to project a positive self-image, and symptom exaggeration, while negatively associated with perseverance and task-oriented coping with stress. The classification accuracy of the model was 73.2%. Sten norms were developed for all diagnostic scales. Conclusions: The Psycho-Vox questionnaire is characterised by good construct validity, high reliability, and practical clinical usefulness. The tool can be used in the differential diagnosis of muscle tension dysphonia and in monitoring the progress of treatment and rehabilitation. Full article

Background/Objectives: Laryngeal leukoplakia and dysplasia carry a variable risk of malignant transformation. Although microlaryngoscopic excision is standard of care, data on voice function are limited. Multidimensional diagnostics, including the Vocal Extent Measure (VEM), were employed to assess pre- and postoperative status while identifying factors associated with vocal outcomes. Methods: This retrospective cohort included 44 patients with histologically confirmed vocal fold leukoplakia or dysplasia. All underwent cold steel or laser-assisted phonomicrosurgery. Voice assessments were conducted pre- and three months postoperatively, comprising videolaryngostroboscopy, auditory-perceptual evaluation of grade, roughness and breathiness (GRB), self-assessment (Voice Handicap Index, VHI-9i), and objective acoustic-aerodynamic measures. Results: Overall, 57% of patients were active smokers; 73% consumed alcohol. Lesions were mostly unilateral (77%), craniomedially localized (65%), and involved up to one-third of the vocal fold (48%), with impaired mucosal wave (76%). Histopathology revealed mainly hyperkeratosis (52%) and dysplasia (35%). Recurrence rate was 14%, with histology unchanged. Postoperatively, subjective measures showed significant improvements (post- vs. preoperative), with decreased VHI-9i scores (10 vs. 14) and GRB ratings (p < 0.05). Objective measures showed positive trends, including enhanced vocal capacity (VEM 85 vs. 82), stability (jitter 0.6 vs. 0.8%), and aerodynamics (maximum phonation time 18 vs. 15 s). Phonosurgical method, histopathology, and age did not significantly affect voice outcomes; however, higher dysplasia grades and younger age showed trends toward greater VEM gains. Conclusions: Phonomicrosurgical excision of laryngeal leukoplakia and dysplasia effectively preserves or enhances vocal function. The VEM provides a reliable, quantitative complement to established voice diagnostics and should be integrated into standardized assessment protocols. Full article

This practice-based implementation report describes the adoption of the My Abilities First (MAF) initiative for children with developmental delays in Taiwan. Grounded in the International Classification of Functioning, Disability and Health (ICF) framework, MAF emphasizes a strengths-based, participatory, and human rights-oriented approach to early childhood intervention. The purpose of this report is to describe the development of the MAF framework and the details of its innovative, culturally sensitive implementation in Taiwan, using implementation science principles to support the national adoption of My Abilities ID Cards (ABIDs). Central to the MAF initiative is the ABID, a tool that empowers children to express their abilities, preferences, and support needs using their own voice or preferred mode of communication. Guided by implementation science, the MAF team in Taiwan engaged stakeholders in urban and rural centers, developed training programs, and integrated ABID into early intervention and special education systems. Preliminary outcomes indicate that from 2021 to 2025, 140 training sessions reached a total attendance of 6961. Notably, satisfaction with training was high (>95%), and practitioner subjective competence adopting positive language improved. The number of children under age 12 creating ABIDs grew to approximately 700. Preliminary evidence suggests that ABIDs might increase systematic adoption of children’s opinions in assessments and interventions. Qualitative feedback from parents and professionals highlights the contribution of ABIDs, ensuring self-expression, motivation, and meaningful participation. The pioneering Taiwanese experience demonstrates the feasibility and impact of MAF and ABIDs in promoting children’s rights and participation, offering practical insights for global adaptation in diverse contexts. Full article

The growing need to safeguard sensitive data in various fields, including in relation to education, banking over the phone, private voice conferences, and the military, has grown as dependence on technology in daily life has increased. Encryption schemes based on chaotic systems are among the most commonly utilized approaches in the security field due to their high levels of safety and reliability. This study proposes a secure audio encryption framework based on the Chameleon chaotic algorithm implemented on a Xilinx ZedBoard Zynq-7000 FPGA. The system was designed using a fixed-point arithmetic format with 32-bit precision (eight integers; 24 fractional bits) with the Xilinx System Generator in MATLAB Simulink R2021b and verified using Vivado. The Chameleon Chaotic System, characterized by its transition from self-excited to hidden attractors through parameter variation, adds complexity to the system dynamics and strengthens the encryption algorithm. The Adaptive Feedback Control technique was applied to synchronize the signals. These methods enhance the security of audio data by ensuring robust and fast synchronization during transmission. The performance of the proposed system was assessed using correlation analysis, the mean squared error, histogram analysis, and audio spectrogram analysis. The system demonstrated strong encryption capabilities with low correlation values (−0.0033). In decryption, they achieved high fidelity with a correlation exceeding 0.999 in noise-free conditions and above 0.9933 under 20 dB AWGN. Adaptive Feedback Control showed superior decryption precision with lower MSEU and higher PSNR, confirming its effectiveness under noisy environments. Full article

This study examines how a voice-recognition project may support vocational secondary students’ AI literacy. In this applied scenario, students used Arduino hardware and an AI tools platform to collect data, train models, and deploy a basic voice-recognition device, linking introductory AI concepts with practical engineering applications. A mixed-methods design combined pre–post self-report assessment using the AI Literacy Questionnaire (AILQ) with post semi-structured interviews. Emerging gains were associated with the maker-learning pathway, particularly in the affective, behavioral, and cognitive AI literacy domains, whereas ethical outcomes were limited within this intervention window. Qualitative insights provided complementary interpretive context, suggesting that learning through making was experienced as more engaging and personally relevant, while hands-on linked with emerging understanding of AI model behavior and limitations. Overall, the study extends AI-literacy research to a vocational classroom setting, where evidence remains limited. It also highlights a domain-level AI literacy analysis for identifying which components strengthen through making and which may require more explicit instructional scaffolding in this specific vocational context. The exploratory nature of the study offers evidence that maker activities can provide a feasible approach for engaging vocational learners with multidimensional AI literacy. Full article

Background: To develop and validate an automated visual acuity (VA) testing system integrating artificial intelligence (AI)–driven speech and image recognition technologies, enabling self-administered, clinic-based VA assessment; Methods: The system incorporated a fine-tuned Whisper speech-recognition model with Silero voice activity detection and pose estimation through facial landmark and ArUco marker detection. A state-driven interface guided users through sequential testing with and without a pinhole. Speech recognition was enhanced using a local Singaporean English dataset. Laboratory validation assessed speech and pose recognition performance, while clinical validation compared automated and manual VA testing at a tertiary eye clinic; Results: The fine-tuned model reduced word error rates from 17.83% to 9.81% for letters and 2.76% to 1.97% for numbers. Pose detection accurately identified valid occluder states. Among 72 participants (144 eyes), automated unaided VA showed good agreement with manual VA (ICC = 0.77, 95% CI 0.62–0.85), while pinhole VA demonstrated moderate agreement (ICC = 0.63, 95% CI 0.25–0.83). Automated testing took longer (132.1 ± 47.5 s vs. 97.1 ± 47.8 s; p < 0.001), but user experience remained positive (mean Likert scale score 4.3 ± 0.8); Conclusions: The AI-based automated VA system delivered accurate, reliable, and user-friendly performance, supporting its feasibility for clinical implementation. Full article

Various sensors are increasingly being adopted to support intelligent healthcare systems, which address the growing problem of staff shortages in assisted-living communities. In this context, detecting and assessing pain remain critical yet challenging tasks in both clinical and non-clinical settings. Traditional approaches such as self-reporting, physiological signal monitoring, and facial expression analysis often face limitations related to accessibility, equipment costs, and the need for professional support. To overcome these challenges in this work, we investigate a sensor-assisted system for pain detection and propose a lightweight framework that enables real-time classification of pain levels using acoustic sensors. Our system exploits the spectral features of voice signals that strongly correlate with pain to train Convolutional Neural Network (CNN) models. Our system has been validated through simulations in Jupiter Notebook and a Raspberry Pi-based hardware prototype. The experimental results demonstrate that the proposed three-level pain classification approach obtains an average accuracy of 72.74%, outperforming existing methods with the same pain-level granularity by 18.94–26.74% and achieving performance comparable to that of binary pain detection methods. Our hardware prototype, built from commercial off-the-shelf components for under 100 USD, achieves real-time processing speeds ranging from approximately 6 to 22 s. In addition to CNN models, our experiments demonstrate that other machine learning algorithms, such as Artificial Neural Networks, XGBoost, Random Forests, and Decision Trees, also prove to be applicable within our pain level classification framework. Full article

Background/Objectives: The aim of this study was to assess the short-term and long-term effectiveness of group voice therapy in changing vocal behaviour and improving voice quality (VQ) among teachers with hyperfunctional voice disorders (HFVD), using both subjective and objective measures. Methods: Thirty-one teachers participated in a structured group voice therapy programme. Participants underwent videoendostroboscopic evaluation of laryngeal morphology and function, perceptual assessment of voice, acoustic analysis of voice samples, and aerodynamic measurements of phonation. Patients’ self-assessment of VQ and its impact on quality of life were measured using a Visual Analogue Scale (VAS) and the Voice Handicap Index-30 (VHI-30). Evaluations were conducted at four time points: pre-therapy (T0), immediately post-therapy (T1), and at 3-month (T3) and 12-month (T12) follow-up visits. Results: Significant improvement was observed between T0 and T1 in perceptual voice evaluations: grade, roughness, asthenia, strain, loudness, fast speaking rate, as well as in neck muscle tension, shimmer, patients’ most harmful vocal behaviours, VHI-30 scores, patients VQ evaluation, and its impact on quality of life (all p < 0.05). Almost all parameters of subjective and objective voice assessment improved over the 12-month observation period, with the greatest improvement between T0 and T12 (all p < 0.05), indicating lasting reduced laryngeal tension and improved phonatory efficiency. Conclusions: Group voice therapy has been shown to be an effective treatment for teachers with HFVD, leading to significant and long-lasting improvements in perceptual, acoustic, and self-assessment outcomes. Therapy also promoted healthier vocal and lifestyle behaviours, supporting its role as a successful and cost-effective rehabilitation and prevention method for occupational voice disorders. Full article

Self-supervised learning (SSL) models have achieved remarkable success in speaker verification tasks, yet their robustness to real-world audio degradation remains insufficiently characterized. This study presents a comprehensive analysis of how audio quality degradation affects three prominent SSL-based speaker verification systems (WavLM, Wav2Vec2, and HuBERT) across three diverse datasets: TIMIT, CHiME-6, and Common Voice. We systematically applied 21 degradation conditions spanning noise contamination (SNR levels from 0 to 20 dB), reverberation (RT60 from 0.3 to 1.0 s), and codec compression (various bit rates), then measured both objective audio quality metrics (PESQ, STOI, SNR, SegSNR, fwSNRseg, jitter, shimmer, HNR) and speaker verification performance metrics (EER, AUC-ROC, d-prime, minDCF). At the condition level, multiple regression with all eight quality metrics explained up to 80% of the variance in minDCF for HuBERT and 78% for WavLM, but only 35% for Wav2Vec2; EER predictability was lower (69%, 67%, and 28%, respectively). PESQ was the strongest single predictor for WavLM and HuBERT, while Shimmer showed the highest single-metric correlation for Wav2Vec2; fwSNRseg yielded the top single-metric R² for WavLM, and PESQ for HuBERT and Wav2Vec2 (with much smaller gains for Wav2Vec2). WavLM and HuBERT exhibited more predictable quality-performance relationships compared to Wav2Vec2. These findings establish quantitative relationships between measurable audio quality and speaker verification accuracy at the condition level, though substantial within-condition variability limits utterance-level prediction accuracy. Full article

Acoustic voice analysis demonstrates potential as a non-invasive biomarker for depression, yet its generalizability across languages remains underexplored. This cross-sectional study aimed to identify a set of cross-culturally consistent acoustic features for depression screening using distinct Vietnamese and Japanese voice datasets. We analyzed anonymized recordings from 251 participants, comprising 123 Vietnamese individuals assessed via the self-report Beck Depression Inventory (BDI) and 128 Japanese individuals assessed via the clinician-rated Hamilton Depression Rating Scale (HAM-D). From 6373 features extracted with openSMILE, a multi-stage selection pipeline identified 12 cross-cultural features, primarily from the auditory spectrum (AudSpec), Mel-Frequency Cepstral Coefficients (MFCCs), and logarithmic Harmonics-to-Noise Ratio (logHNR) domains. The cross-cultural model achieved a combined Area Under the Curve (AUC) of 0.934, with performance disparities observed between the Japanese (AUC = 0.993) and Vietnamese (AUC = 0.913) cohorts. This disparity may be attributed to dataset heterogeneity, including mismatched diagnostic tools and differing sample compositions (clinical vs. mixed community). Furthermore, the limited number of high-risk cases (n = 33) warrants cautious interpretation regarding the reliability of reported AUC values for severe depression classification. These findings suggest the presence of a core acoustic signature related to physiological psychomotor changes that may transcend linguistic boundaries. This study advances the exploration of global vocal biomarkers but underscores the need for prospective, standardized multilingual trials to overcome the limitations of secondary data analysis. Full article