Search Results (551)

Background/Objectives: Identifying and classifying abnormal lung sounds is essential for diagnosing patients with respiratory disorders. In particular, the simultaneous recording of auscultation signals from multiple clinically relevant positions offers greater diagnostic potential compared to traditional single-channel measurements. This study aims to improve the accuracy of respiratory sound classification by leveraging multichannel signals and capturing positional characteristics from multiple sites in the same patient. Methods: We evaluated the performance of respiratory sound classification using multichannel lung sound data with a deep learning model that combines a convolutional neural network (CNN) and long short-term memory (LSTM), based on mel-frequency cepstral coefficients (MFCCs). We analyzed the impact of the number and placement of channels on classification performance. Results: The results demonstrated that using four-channel recordings improved accuracy, sensitivity, specificity, precision, and F1-score by approximately 1.11, 1.15, 1.05, 1.08, and 1.13 times, respectively, compared to using three, two, or single-channel recordings. Conclusions: This study confirms that multichannel data capture a richer set of features corresponding to various respiratory sound characteristics, leading to significantly improved classification performance. The proposed method holds promise for enhancing sound classification accuracy not only in clinical applications but also in broader domains such as speech and audio processing. Full article

Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding and detection of various pathological conditions, extending beyond traditional speech analysis to encompass other forms of acoustic data. A particularly promising and rapidly evolving area is the application of deep learning techniques for the detection and analysis of diverse pathologies, including respiratory, cardiac, and neurological disorders, through sound processing. This paper provides a comprehensive review of the current state-of-the-art in using deep learning for pathology detection via analysis of biological sounds. It highlights key successes achieved in the field, identifies existing challenges and limitations, and discusses potential future research directions. This review aims to serve as a valuable resource for researchers and clinicians working in this interdisciplinary domain. Full article

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article

Background/Objective: Puberphonia is a voice disorder characterized by the persistence of a high-pitched voice in sexually mature males. In phoniatrics and speech-language pathology, it is also known as post-mutational voice instability, mutational falsetto, persistent fistulous voice, or functional falsetto. The absence of an age-appropriate vocal pitch may adversely affect psychological well-being and hinder personal, social, and occupational functioning. The aim of this study was to evaluate of the impact of phoniatric and logopedic rehabilitation on voice quality in patients with puberphonia. Methods: The study included 18 male patients, aged 16 to 34 years, rehabilitated for voice mutation disorders. Phoniatric and logopedic rehabilitation included voice therapy tailored to each subject. A logopedist led exercises aimed at lowering and stabilizing the pitch of the voice and improving its quality. A phoniatrician supervised the therapy, monitoring the condition of the vocal apparatus and providing additional diagnostic and therapeutic recommendations as needed. The duration and intensity of the therapy were adjusted for each patient. Before and after voice rehabilitation, the subjects completed the following questionnaires: the Voice Handicap Index (VHI), the Vocal Tract Discomfort (VTD) scale, and the Voice-Related Quality of Life (V-RQOL). They also underwent an acoustic voice analysis. Results: Statistical analysis of the VHI, VTD, and V-RQOL scores, as well as the voice’s acoustic parameters, showed statistically significant differences before and after rehabilitation (p < 0.005). Conclusions: Phoniatric and logopedic rehabilitation is an effective method of reducing and maintaining a stable, euphonic male voice in patients with functional puberphonia. Effective voice therapy positively impacts selected aspects of psychosocial functioning reported by patients, improves voice-related quality of life, and reduces physical discomfort in the vocal tract. Full article

Background/Objectives: Accurate assessment of speech intelligibility is necessary for individuals with motor speech disorders. Transcription or scaled rating methods by naïve listeners are the most reliable tasks for these purposes; however, they are often resource-intensive and time-consuming within clinical contexts. Automatic speech recognition (ASR) systems, which transcribe speech into text, have been increasingly utilized for assessing speech intelligibility. This study investigates the feasibility of using an open-source ASR system to assess speech intelligibility in Hebrew and English speakers with Down syndrome (DS). Methods: Recordings from 65 Hebrew- and English-speaking participants were included: 33 speakers with DS and 32 typically developing (TD) peers. Speech samples (words, sentences) were transcribed using Whisper (OpenAI) and by naïve listeners. The proportion of agreement between ASR transcriptions and those of naïve listeners was compared across speaker groups (TD, DS) and languages (Hebrew, English) for word-level data. Further comparisons for Hebrew speakers were conducted across speaker groups and stimuli (words, sentences). Results: The strength of the correlation between listener and ASR transcription scores varied across languages, and was higher for English (r = 0.98) than for Hebrew (r = 0.81) for speakers with DS. A higher proportion of listener–ASR agreement was demonstrated for TD speakers, as compared to those with DS (0.94 vs. 0.74, respectively), and for English, in comparison to Hebrew speakers (0.91 for English DS speakers vs. 0.74 for Hebrew DS speakers). Listener–ASR agreement for single words was consistently higher than for sentences among Hebrew speakers. Speakers’ intelligibility influenced word-level agreement among Hebrew- but not English-speaking participants with DS. Conclusions: ASR performance for English closely approximated that of naïve listeners, suggesting potential near-future clinical applicability within single-word intelligibility assessment. In contrast, a lower proportion of agreement between human listeners and ASR for Hebrew speech indicates that broader clinical implementation may require further training of ASR models in this language. Full article

Selective Mutism (SM) is a childhood anxiety disorder characterized by the persistent inability to speak in specific social settings while being able to speak freely in more comfortable environments, such as at home with family. This condition often leads to significant impairments in social, academic, and occupational functions. This article presents a novel treatment methodology that integrates the physiology of vocal production with pragmatic language instruction through teletherapy, administered to two adolescents diagnosed with selective mutism (SM). The frequency of speaking on the Selective Mutism Questionnaire increased from 35% to 86% and from 25% to 55% for the two children. Pragmatic language skills on the Social Communication Skills: Pragmatics Checklist improved from 47% to 96% and 13% to 40% after treatment. It is crucial to emphasize vocal control for speech initiation and pragmatic language for verbal expression. Detailed strategies, specific activities, and treatment outcomes are provided. Full article

Background/Objectives: Communication disorders in childhood, including expressive, receptive, pragmatic, and fluency impairments, have been consistently linked to mental health challenges such as anxiety, depression, and behavioural difficulties. However, existing research remains fragmented across diagnostic categories and developmental stages. This scoping review aimed to synthesise empirical evidence on the relationship between communication disorders and mental health outcomes in children and adolescents and to identify key patterns and implications for practice and policy. Methods: Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and Arksey and O’Malley’s framework, this review included empirical studies published in English between 2000 and 2024. Five databases were searched, and ten studies met the inclusion criteria. Data were charted and thematically analysed to explore associations across communication profiles and emotional–behavioural outcomes. Results: Four interconnected themes were identified: (1) emotional and behavioural manifestations of communication disorders; (2) social burden linked to pragmatic and expressive difficulties; (3) family and environmental stressors exacerbating child-level challenges; and (4) a lack of integrated care models addressing both communication and mental health needs. The findings highlight that communication disorders frequently co-occur with emotional difficulties, often embedded within broader social and systemic contexts. Conclusions: This review underscores the need for developmentally informed, culturally responsive, and interdisciplinary service models that address both communication and mental health in children. Early identification, family-centred care, and policy reforms are critical to reducing inequities and improving outcomes for this underserved population. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

The human voice is an invaluable tool for communication, carrying information about a speaker’s emotional state and cognitive health. Recent research highlights the potential of acoustic biomarkers to detect early signs of mental health and neurodegenerative conditions. Despite their promise, vocal biomarkers remain underutilized in clinical settings, with limited standardized protocols for assessment. This Perspective article argues for the integration of acoustic biomarkers into digital health solutions to improve the detection and monitoring of cognitive impairment and emotional disturbances. Advances in speech analysis and machine learning have demonstrated the feasibility of using voice features such as pitch, jitter, shimmer, and speech rate to assess these conditions. Moreover, we propose that singing, particularly simple melodic structures, could be an effective and accessible means of gathering vocal biomarkers, offering additional insights into cognitive and emotional states. Given its potential to engage multiple neural networks, singing could function as an assessment tool and an intervention strategy for individuals with cognitive decline. We highlight the necessity of further research to establish robust, reproducible methodologies for analyzing vocal biomarkers and standardizing voice-based diagnostic approaches. By integrating vocal analysis into routine health assessments, clinicians and researchers could significantly advance early detection and personalized interventions for cognitive and emotional disorders. Full article

Currently, high-tech assistive technologies (ATs), particularly Socially Assistive Robots (SARs), virtual reality (VR) and conversational AI (ConvAI), are considered very useful in supporting professionals in Speech and Language Therapy (SLT) for children with communication disorders. However, despite a positive public perception, therapists face difficulties when integrating these technologies into practice due to technical challenges and a lack of user-friendly interfaces. To address this gap, a design-based research approach has been employed to streamline the integration of SARs, VR and ConvAI in SLT, and a new software platform called “ATLog” has been developed for designing interactive and playful learning scenarios with ATs. ATLog’s main features include visual-based programming with graphical interface, enabling therapists to intuitively create personalized interactive scenarios without advanced programming skills. The platform follows a subprocess-oriented design, breaking down SAR skills and VR scenarios into microskills represented by pre-programmed graphical blocks, tailored to specific treatment domains, therapy goals, and language skill levels. The ATLog platform was evaluated by 27 SLT experts using the Technology Acceptance Model (TAM) and System Usability Scale (SUS) questionnaires, extended with additional questions specifically focused on ATLog structure and functionalities. According to the SUS results, most of the experts (74%) evaluated ATLog with grades over 70, indicating high acceptance of its usability. Over half (52%) of the experts rated the additional questions focused on ATLog’s structure and functionalities in the A range (90–100), while 26% rated them in the B range (80–89), showing strong acceptance of the platform for creating and running personalized interactive scenarios with ATs. According to the TAM results, experts gave high grades for both perceived usefulness (44% in the A range) and perceived ease of use (63% in the A range). Full article

Background/Objectives: The Motor Speech Hierarchy (MSH) Probe Words (PWs) have yet to be validated as effective in discriminating between children with impaired and children with typically developing speech motor control. This preliminary study first examined the effectiveness of the mandibular control subtest of the MSH-PWs in distinguishing between typically developing (TD) and speech sound-disordered (SSD) children aged between 3 years 0 months and 3 years 6 months. Secondly, we compared automatically derived kinematic measures of jaw range and control with MSH-PW consensus scoring to assist in identifying deficits in mandibular control. Methods: Forty-one children with TD speech and 13 with SSD produced the 10 words of the mandibular stage of the MSH-PWs. A consensus team of speech pathologists observed video recordings of the words to score motor speech control and phonetic accuracy, as detailed in the MSH-PW scoring criteria. Specific measures of jaw and lip movements during speech were also extracted to derive the objective measurements, with agreement between the perceptual and objective measures of jaw range and jaw control evaluated. Results: A significant difference between TD and SSD groups was found for jaw range (p = 0.006), voicing transitions (p = 0.004) and total mandibular scores (p = 0.015). SSD and TD group discrimination was significant (at alpha = 0.01) with a balanced classification accuracy of 0.79. Initial analysis indicates objective kinematic measures using facial tracking show good agreement with perceptual judgements of jaw range and jaw control. Conclusions: The preliminary data indicate the MSH-PWs can discriminate TD speech from SSD at the level of mandibular control and can be used by clinicians to assess motor speech control. Further investigation of objective measures to support perceptual scoring is indicated. Full article

Cerebellar ataxias are a group of disorders characterized by clumsy movements because of defective muscle control. In affected individuals, muscular impairment might have an impact on activities like walking, balance, hand coordination, speech, and feeding, as well as eye movements. The development of symptoms typically takes place during the span of adolescence, and it has the potential to cause distress for individuals in many areas of their lives, including professional and interpersonal relationships. Although skeletal muscle is understudied in ataxias, its examination may provide hitherto unexplored details in this family of disorders. Observing muscle involvement can assist in diagnosing conditions where genetic tests alone are inconclusive. Furthermore, it helps determine the stage of progression of a pathology that might otherwise be challenging to assess. In this study, we reviewed the main scientific literature reporting on skeletal muscle examination in autosomal recessive cerebellar ataxias (ARCAs), with a focus on the rare Marinesco–Sjögren syndrome. (MSS). Our aim was to highlight the similarities in muscle alterations observed in ARCA patients while also considering data gathered from preclinical models. Analyzing the similarities among these disorders could enhance our understanding of the unidentified mechanisms underlying the phenotypic evolution of some less common conditions. Full article

Background/Objectives: Menière’s disease (MD) is a debilitating disorder with episodic and variable ear symptoms. Diagnosis can be challenging, and evidence for therapeutic approaches is low. Furthermore, patients show a unique and fluctuating configuration of audiovestibular impairment. As a psychometric instrument to assess hearing-specific disability is currently lacking, we evaluated a short form of the Speech, Spatial, and Qualities of Hearing Scale (SSQ) in a cohort of patients with MD. Methods: Data was collected in the context of a multicenter prospective patient registry intended for the long-term follow up of MD patients. Hearing was assessed by pure tone and speech audiometry. The SSQ was applied in the German language version with 17 items. Results: In total, 97 consecutive patients with unilateral MD with a mean age of 56.2 ± 5.0 years were included. A total of 55 individuals (57.3%) were female, and 72 (75.0%) were categorized as having definite MD. The average total score of the SSQ was 6.0 ± 2.1. Cronbach’s alpha for internal consistency was 0.960 for the total score. We did not observe undue floor or ceiling effects. SSQ values showed a statistically negative correlation with hearing thresholds and a statistically positive correlation with speech recognition scores of affected ears. Conclusions: The short form of the SSQ provides insight into hearing-specific disability in patients with MD. Therefore, it may be informative regarding disease stage and rehabilitation needs. Full article

Background: Several studies document the importance of communicative abilities for children’s development. Especially in recent years verbal communication in preschool children with developmental language disorder (DLD) has been studied, relying heavily on statistical analysis, outcome measures, or/and parents’ reports. Purpose: This explorative study investigates the effects of speech therapy on the development of language and verbal communication skills in preschool children with DLD within their peer group in a day-to-day setting using objective video-documentation. Hypothesis: Speech therapy leads to improvement of language, communication, and possibly to concurrent development of both language and verbal communication skills in preschool children. Methods: Preliminary prospective study to assess language and verbal communications skills of nine preschool children (seven boys, two girls, 4–6 y) with DLD in a speech therapy kindergarten using video recordings over a one-year therapy period. The communicative participation of the members of the peer group was assessed and included the verbal address (Av) and the ratio of “verbal address/verbal reaction” (Av/Rv). Results: The investigation results in evidence for two outcome groups: One group with suspected preferential verbal communication disorders (n = 4) was characterised by a high Av/Rv value, meaning they were scored to have a normal or high verbal address (Av) and a low verbal response (Rv) (predominantly interpersonal communication related disorder). This group showed minimal changes in the short term but demonstrated improvement after 5 years of schooling; thus, pedagogical activities seemed to be particularly effective for these children. The second group showed a balanced Av/Rv ratio (predominantly language related disorder) (n = 5); but after five years they demonstrated a partial need for special school support measures. This group may therefore particularly benefit from speech therapy. Conclusions: The present study clearly shows that even with speech-language therapy, the linguistic ability of DLD-disturbed children does not necessarily develop simultaneously with their communication ability. Rather, the investigations provide evidence for two groups of preschool children with DLD and communication disorder: One group demonstrated a predominantly verbal communication related disorder, where pedagogical intervention might be the more important treatment. The second group showed predominantly DLD, therefore making speech therapy the more effective intervention. In this study, all children expressed their desire to communicate with their peers. To the authors’ best knowledge, this is the first study determining the ability to communicate in a preschool cohort with DLD using characterisation with video documentation in a follow-up for 1 year. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article