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BackgroundBrain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.MethodsA 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.ResultsOn the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.ConclusionsIn a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

It is debated whether primary progressive apraxia of speech (PPAOS) and progressive agrammatic aphasia (PAA) belong to the same clinical spectrum, traditionally termed non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), or exist as two completely distinct syndromic entities with specific pathologic/prognostic correlates. We analysed speech, language and disease severity features in a comprehensive cohort of patients with progressive motor speech impairment and/or agrammatism to ascertain evidence of naturally occurring, clinically meaningful non-overlapping syndromic entities (e.g. PPAOS and PAA) in our data. We also assessed if data-driven latent clinical dimensions with aetiologic/prognostic value could be identified. We included 98 participants, 43 of whom had an autopsy-confirmed neuropathological diagnosis. Speech pathologists assessed motor speech features indicative of dysarthria and apraxia of speech (AOS). Quantitative expressive/receptive agrammatism measures were obtained and compared with healthy controls. Baseline and longitudinal disease severity was evaluated using the Clinical Dementia Rating Sum of Boxes (CDR-SB). We investigated the data's clustering tendency and cluster stability to form robust symptom clusters and employed principal component analysis to extract data-driven latent clinical dimensions (LCD). The longitudinal CDR-SB change was estimated using linear mixed-effects models. Of the participants included in this study, 93 conformed to previously reported clinical profiles (75 with AOS and agrammatism, 12 PPAOS and six PAA). The remaining five participants were characterized by non-fluent speech, executive dysfunction and dysarthria without apraxia of speech or frank agrammatism. No baseline clinical features differentiated between frontotemporal lobar degeneration neuropathological subgroups. The Hopkins statistic demonstrated a low cluster tendency in the entire sample (0.45 with values near 0.5 indicating random data). Cluster stability analyses showed that only two robust subgroups (differing in agrammatism, executive dysfunction and overall disease severity) could be identified. Three data-driven components accounted for 71% of the variance [(i) severity-agrammatism; (ii) prominent AOS; and (iii) prominent dysarthria]. None of these data-driven LCDs allowed an accurate prediction of neuropathology. The severity-agrammatism component was an independent predictor of a faster CDR-SB increase in all the participants. Higher dysarthria severity, reduced words per minute and expressive and receptive agrammatism severity at baseline independently predicted accelerated disease progression. Our findings indicate that PPAOS and PAA, rather than exist as completely distinct syndromic entities, constitute a clinical continuum. In our cohort, splitting the nfvPPA spectrum into separate clinical phenotypes did not improve clinical-pathological correlations, stressing the need for new biological markers and consensus regarding updated terminology and clinical classification.

Parkinson's disease is related to nervous system disorders in human beings. In this paper, the status of Parkinson's disease has been predicted based on the analysis of features of two Parkinson's disease symptoms: Dysphonia and Dysarthria. The Dysphonia symptom has been measured by analyzing the features extracted from the vocal sounds of a patient. The Dysarthria symptom of Parkinson's disease has been analyzed by the features extracted from the speech signals of a patient. Hence, two different prediction models have been built for deriving the status of Parkinson's disease. The derivation of these prediction systems is based on statistical and discriminant feature analysis. The statistical-based feature analysis is carried out using descriptive and inferential analysis. In contrast, the enteric, deviation, and correlation-based information between the features are utilized for descriptive analysis. The inferential analysis has been performed using hypothetical testing of the distribution of features. The descriptive and inferential feature analyses are used to derive effective features for the Parkinson's disease problem based on the Dysphonia and Dysarthria symptoms. In the discriminant feature analysis, the feature sets undergo deriving discriminant features. The best-selected features are used to build a prediction model for detecting Parkinson's disease problems in a patient. Extensive experimentation has been carried out using a Dysphonia dataset containing 195 samples. Each sample has 23 features. One dataset for Dysarthria symptoms contains 756 observations, with each observation having 754 features. The best prediction models that have been obtained correspond to these datasets, and 93.50% is obtained for Dysphonia and 87.63% for Dysarthria symptoms databases, respectively. The performance of the proposed system has been compared with some existing methods concerning each employed dataset, showing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

One of the dysarthria features is inaccurate articulation of consonants and vowels, which is the main cause of speech unintelligibility. However, it is not clear what acoustic measures are more related to speech intelligibility in dysarthria. On the other hand, the number of vowels, their articulation and acoustic characteristics are various in different languages. As far as we know, no study investigated vowels articulation in developmental dysarthria in Persian, so there is not enough information about movement details which make their inaccuracies articulation. Hence, the present study aimed to investigate some acoustic measures including vowel formants, vowel space area (VSA), formant centralization ratio (FCR) and vowel articulation index (VAI) and aim to study their correlation with speech intelligibility in Persian speaking children with dysarthria due to spastic cerebral palsy. In this cross ـ sectional study, 11 monolingual children with dysarthria aged 3 ـ 9 years and their typically developing matched peers were selected through convenience sampling. For each of the six Persian vowels, two monosyllable words were acoustically analyzed using PRAAT software. To determine intelligibility of words and vowels, the percentage of correct detected ones was calculated. Results showed a significant difference between the two groups in the third formant of the vowels /a/ and /u/ and the second formant of the vowel /o/. The vowel space area and intelligibility were significantly reduced in dysarthria. VAS was more correlated with intelligibility and is a good acoustic measurement to assess speech intelligibility in dysarthria. However all three studied acoustic indices had moderate significant correlation with intelligibility. [ABSTRACT FROM AUTHOR]

Myasthenia gravis (MG) is a neuromuscular disease of autoimmune etiology and chronic evolution. In addition to the muscle weakness and fatigue that characterize MG, in some studies patients show an inferior performance in cognitive tasks and difficulties in recognizing basic emotions from facial expressions. However, it remains unclear if these difficulties are due to anxious–depressive symptoms that these patients present or related to cognitive abilities, such as facial recognition. This study had a descriptive cross-sectional design with a sample of 92 participants, 52 patients with MG and 40 healthy controls. The data collection protocol included measures to assess recognition of facial expressions (BRFT), facial emotional expression (FEEL), and levels of anxiety and depression (HADS). The MG group had worse performance than the control group in recognizing "fear" (p = 0.001; r = 0.344), "happiness" (p = 0.000; r = 0.580), "disgust" (p = 0.000; r = 0.399), "surprise" (p = 0.000; r = 0.602), and "anger" (p = 0.007; r = 0.284). Likewise, the MG group also underperformed in facial recognition (p = 0.001; r = 0.338). These difficulties were not related to their levels of anxiety and depression. Alterations were observed both in the recognition of facial emotions and in facial recognition, without being mediated by emotional variables. These difficulties can influence the interpersonal interaction of patients with MG. [ABSTRACT FROM AUTHOR]

Introduction: Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework. Methods: The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants' speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed. Results: Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability. Discussion: The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation. [ABSTRACT FROM AUTHOR]

Introduction: Acute confusional migraine (ACM) is a rare variant of migraine that is benign and selfresolving but difficult to diagnose. Without known causative pathophysiology and a lack of recognition in the International Classification of Headache Disorders (ICHD-3), ACM offers a puzzling clinical presentation. There currently is no standardized treatment for ACM, but with a growing anecdotal dataset there is the opportunity to formally recognize and establish protocols to improve patient care and outcomes. Case Report: A 14-year-old female presented to the emergency department (ED) with acute onset of confusion, vision changes, right-sided weakness, and dysarthria one hour prior to arrival. A stroke workup at the initial ED offered no pertinent findings. The patient was transferred to a pediatric specialty ED where all symptoms, aside from numbness and a mild headache, resolved during transfer. After administration of a migraine cocktail at the pediatric specialty ED, all remaining symptoms completely resolved. The patient was discharged home from the ED the same evening with outpatient follow-up. Conclusion: This case presents the difficulty of diagnosing and treating ACM prior to its self-resolution. It highlights the need for formal recognition of the condition by the ICHD-3. In doing so, greater recognition will promote more research, awareness, and establishment of a standardized treatment for ACM. [ABSTRACT FROM AUTHOR]

Purpose: Traditional health care decision-making models center on clinicians making decisions for patients/caregivers based on the best available research evidence. However, this diminishes patient/caregiver involvement in their care and hinders the ability to align care plans with patient values and preferences. Shared decision making is a potentially beneficial process to implement with individuals with amyotrophic lateral sclerosis (ALS) to provide more holistic, patient-centered dysarthria and dysphagia treatment. Shared decision making promotes active involvement by patients/caregivers by informing them of potential treatment options, understanding their values and preferences, and aligning their desires with treatment options to determine the most optimal individualized care plan. Conclusions: The benefits and barriers to incorporating shared decision making within ALS multidisciplinary clinics are discussed in this clinical focus article. Furthermore, a fictional case study example of how to apply shared decision making to dysarthria and dysphagia management of individuals with ALS is provided. [ABSTRACT FROM AUTHOR]

Purpose: This case study measured how well the Lee Silverman Voice Treatment (LSVT) improved vocal features, intelligibility, and communicative effectiveness for a multilingual participant with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy. LSVT treatment was chosen for the participant due to the strengths and deficits he presented with prior to treatment, and for the anticipated challenges in treatment that may arise from the presence of multilingualism and impaired cognitive functioning. Methods: A multilingual patient in their 60's (English, Spanish, and French) with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy completed the standard treatment sessions for LSVT. Assessment measures were taken at baseline, immediately post-treatment, and three-months post-treatment. Results: Improvementswere measured in vocal quality, vocal loudness, intelligibility, and communicative effectiveness immediately post-treatment. Three months posttreatment, improvements in vocal quality and intelligibility were maintained. Conclusion: This case study illustrates that LSVT may be a beneficial treatment for complex clients who are multilingual and present with complex comorbidities and cognitive deficits. LSVT resulted in some meaningful changes in vocal quality, intelligibility, and communicative effectiveness for this individual. Clinicians who work with complex patients may wish to consider the theoretical underpinnings of LSVT, client profile, areas of client need, and ability and desire to complete an intensive treatment program to determine if trialing LSVT is appropriate. The use of LSVT with complex clients may yield positive outcomes. [ABSTRACT FROM AUTHOR]

This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject's voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F0), jitter and shimmer were calculated. The F0 standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications. [ABSTRACT FROM AUTHOR]

Speech alterations have been reported in manifest Huntington’s disease (HD) and premanifest mutation carriers (preHD). The aim of our study was to explore these alterations in preHD and whether they can be used as biomarkers. 13 preHD mutation carriers performed reading task, sustained phonation task and syllable repetition tasks at baseline and after 21 months, as well as clinical examination and MRI. Syllable repetition capacity and self-chosen velocity of single syllable repetition differed significantly between time points. There were no changes in clinical ratings or MRI volumetry. Measurements of speech might be sensitive tools for monitoring subclinical changes in preHD. [ABSTRACT FROM AUTHOR]

Background: Myasthenic crisis (MC) is a life-threatening complication of myasthenia gravis (MG), necessitating ventilation. Achieving a safe and timely diagnosis of myasthenic crisis with atypical, isolated presentation is a considerable challenge particularly in elderly patients, where myasthenia gravis can present with isolated dysarthria in rare instances, giving a clinical impression of lacunar stroke. Case presentation: We present a compelling case of a 73-year-old Caucasian female presenting with abrupt onset of isolated dysarthria. Despite initial treatment for a presumed lacunar stroke, subsequent evaluations led to her diagnosis of a myasthenic crisis. Within 72 h of admission, the patient developed dysphagia and shortness of breath, requiring supplemental oxygen. The case highlights the sequential progression of events from the atypical presentation of isolated dysarthria and its course to the management of a myasthenic crisis. Conclusion: Our reported case focuses on the discussion of myasthenia that mimicked a lacunar stroke and was finally diagnosed at a critical time of medical crisis. This case highlights the imperative notion that isolated dysarthria in elderly individuals warrants vigilant monitoring for possible myasthenia gravis, given the low incidence of lacunar stroke presenting with only dysarthria. [ABSTRACT FROM AUTHOR]

Purpose: The current study compared temporal and spectral acoustic contrast between vowel segments produced by speakers with dysarthria across three speech tasks--interactive, solo habitual, and solo clear. Method: Nine speakers with dysarthria secondary to amyotrophic lateral sclerosis participated in the study. Each speaker was paired with a typical interlocutor over videoconferencing software. The speakers produced the vowels /i, ..., ..., æ/ in / h/--vowel-/d/ words. For the solo tasks, speakers read the stimuli aloud in both their habitual and clear speaking styles. For the interactive task, speakers produced a target stimulus for their interlocutor to select among the four possibilities. We measured the duration difference between long and short vowels, as well as the F1/F2 Euclidean distance between adjacent vowels, and also determined how well the vowels could be classified based on their acoustic characteristics. Results: Temporal contrast between long and short vowels was higher in the interactive task than in both solo tasks. Spectral distance between adjacent vowel pairs was also higher for some pairs in the interactive task than the habitual speech task. Finally, vowel classification accuracy was highest in the interactive task. Conclusions: Overall, we found evidence that individuals with dysarthria produced vowels with greater acoustic contrast in structured interactions than they did in solo tasks. Furthermore, the speech adjustments they made to the vowel segments differed from those observed in solo speech. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the effects of the SPEAK OUT! & LOUD Crowd therapy program on speaking rate, percent pause time, intelligibility, naturalness, and communicative participation in individuals with Parkinson's disease (PD). Method: Six adults with PD completed 12 individual SPEAK OUT! sessions across four consecutive weeks followed by group-based LOUD Crowd sessions for five consecutive weeks. Most therapy sessions were conducted via telehealth, with two participants completing the SPEAK OUT! portion in person. Speech samples were recorded at six time points: three baseline time points prior to SPEAK OUT!, two post--SPEAK OUT! time points, and one post--LOUD Crowd time point. Acoustic measures of speaking rate and percent pause time and listener ratings of speech intelligibility and naturalness were obtained for each time point. Participant self-ratings of communicative participation were also collected at pre- and posttreatment time points. Results: Results showed significant improvement in communicative participation scores at a group level following completion of the SPEAK OUT! & LOUD Crowd treatment program. Two participants showed a significant decrease in speaking rate and increase in percent pause time following treatment. Changes in intelligibility and naturalness were not statistically significant. Conclusions: These findings provide preliminary support for the effectiveness of the SPEAK OUT! & LOUD Crowd treatment program in improving communicative participation for people with mild-to-moderate hypokinetic dysarthria secondary to PD. This study is also the first to demonstrate positive effects of this treatment program for people receiving the therapy via telehealth. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this qualitative interview study was to identify themes regarding considerations in the usage of speech amplification device usage for people with Parkinson's disease (PD) and hypophonia from the perspective of speech-language pathologists (SLPs). Method: Eligible participants included SLPs currently practicing in the United States or Canada with experience working with clients with PD for at least 2 years. Ten SLPs participated in 60-min interviews conducted via Zoom. A semistructured interview guide was created prior to the interviews. The interviews were transcribed following their completion, and an iterative coding process was used to identify themes using thematic analysis. Results: Three main themes were identified from the interviews. The first theme encapsulated how clinicians described amplification devices as a potential treatment tool, which highlighted the nuances that may impact selecting an amplification device as a treatment option such as increased hypophonia or dysarthria severity and cognitive decline. The second theme highlighted how device selection depends on the individual needs of the user. Individual client characteristics (such as disease symptoms and individual needs and preferences) may impact the choice of amplification device. The last theme outlined the importance of involving family members in all stages of device use and involving other health care team members on a case-by-case basis. Conclusions: The insights provided by the SLP participants help to understand the clinical decisions that are made when determining device candidacy, selecting a device, and evaluating device success. These insights can be used to improve research studies of augmentative management of hypophonia and guide more personalized management decisions. [ABSTRACT FROM AUTHOR]

The fragmented nature of the medical device market limits our understanding of how particular sub-markets navigate the device development process. Despite the widespread use of transcutaneous neuromuscular electrical stimulation (NMES), its use for acquired dysarthria treatment has not been sufficiently explored. This study aims to provide a preliminary understanding of the stages involved in the development of NMES devices designed for neurorehabilitation. It also aims to investigate manufacturers' perceptions concerning factors that facilitate or impede its development and determine its applicability for acquired dysarthria. In-depth semi-structured online interviews were conducted with eight NMES device manufacturers located across Europe, North America and Oceania. The interviews were video-recorded, automatically transcribed, manually reviewed, and analysed using a qualitative content analysis. NMES device development for neurorehabilitation involves six complex phases with sequential and overlapping activities. Some emerging concepts were comparable to established medical device models, while others were specific to NMES. Its adaptability to different neurological disorders, the positive academia-industry collaborations, the industry's growth prospects and the promising global efforts for standardised regulations are all key facilitators for its development. However, financial, political, regulatory, and natural constraints emerged as barriers. Indications and challenges for the applicability of NMES for acquired dysarthria treatment were also discussed. The findings provide a foundation for further investigations on the NMES market sub-sector, particularly in the context of neurorehabilitation. The study also provides insights into the potential adoption of NMES for acquired dysarthria, which can serve as a reference for future research. The mapped neuromuscular electrical stimulation (NMES) development phases and processes can serve as a framework for new device development initiatives Opportunities for NMES development included its adaptability to neurological disorders, its growth prospects, academia-industry relationships and regulatory standardisation initiatives Financial, political, regulatory, and natural barriers were barriers for concern. From an anatomical and practical standpoint, it seems feasible to use NMES to treat several features of acquired dysarthria, however, clinicians' limited education on the use of electrical stimulation for neurorehabilitation may pose several constraints. [ABSTRACT FROM AUTHOR]

GLUT1 deficiency syndrome (GLUT1DS) is a rare genetic disorder caused by a mutation in the SLC2A1 gene that limits the transport of glucose across the blood-brain barrier. Speech disorders and dysarthria are typical findings in patients with GLUT1DS, but have never been deeply phenotyped. The aim of the present study was to characterize speech abilities in a sample of patients with GLUT1DS. 30 patients with GLUT1DS were recruited. We reported impairments in different speech and oromotor domains: the speech was characterized by dysarthria, inaccurate articulation of consonants, abnormal nasal resonance, errors in intonation and prosody and low intelligibility. We observed difficulties in motor planning and programming. Moreover, we observed a significant difference between the dysarthric level of impairment with genotype groups. The presence of a speech disorder in patients with GLUT1DS represents a core feature of the syndrome. Our findings suggest that patients with GLUT1DS would benefit from a comprehensive neurocognitive assessment to detect strengths and weaknesses of the speech profile. Understanding the speech and language phenotype in GLUT1DS is critical for planning early intervention to positively influence the global development of patients with GLUT1DS. • The purpose of this study is to analyze and characterize the speech profile of a sample of patients with GLUT1DS. • The majority of the patients manifested dysarthria with different levels of severity. • A relationship between genotype and dysartric level of impairment was found. • The introduction of the ketogenic diet therapy might be a protective factor for cognitive outcomes in patients with GLUT1DS. [ABSTRACT FROM AUTHOR]

A 53-year-old patient was admitted to the stroke unit with sudden onset of weakness on the left side and dysarthria. This patient had been diagnosed with symptomatic intracerebral artery stenosis two months previously and had been administered dual oral antiplatelet agents and an aggressive dose of atorvastatin. The patient underwent percutaneous transluminal angioplasty and stenting two weeks after admission. Interventional treatment was technically successful when a self-expandable Enterprise stent was used. She was followed up for 2 years after discharge, without any cerebrovascular incidents. Repeat CTA revealed delayed displacement of the Enterprise stent and a discussion was had. [ABSTRACT FROM AUTHOR]

This paper investigates the nuanced characteristics of the spectral envelope attributes due to vocal-tract resonance structure and fine-level excitation source features within short-term Fourier transform (STFT) magnitude spectra for the assessment of dysarthria. The single-channel convolutional neural network (CNN) employing time-frequency representations such as STFT spectrogram (STFT-SPEC) and Mel-spectrogram (MEL-SPEC) does not ensure capture of the source and system information simultaneously due to the filtering operation using a fixed-size filter. Building upon this observation, this study first explores the significance of convolution filter size in the context of the CNN-based automated dysarthric assessment system. An approach is then introduced to effectively capture resonance structure and fine-level features through a multi-channel CNN. In the proposed approach, the STFT-SPEC is decomposed using a one-level discrete wavelet transform (DWT) to separate the slow-varying spectral structure and fine-level features. The resulting decomposed coefficients in four directions are taken as the inputs to multi-channel CNN to capture the source and system features by employing different sizes of convolution filters. The experimental results conducted on the UA-speech corpus validate the efficacy of the proposed approach utilizing multi-channel CNN. The proposed approach demonstrates the notable enhancement in accuracy and F1 score (60.86% and 48.52%) compared to a single-channel CNN using STFT-SPEC (46.45% and 40.97%), MEL-SPEC (48.86% and 38.20%), and MEL-SPEC appended with delta and delta-delta coefficients (52.40% and 42.84%) for assessment of dysarthria in a speaker-independent and text-independent mode. [ABSTRACT FROM AUTHOR]

Abstract Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

Background: Deep Brain Stimulation (DBS) in the Subthalamic Nucleus (STN) or the Globus Pallidus Interna (GPI) is well-established as a surgical technique for improving global motor function in patients with idiopathic Parkinson’s Disease (PD). Previous research has indicated speech deterioration in more than 30% of patients after STN-DBS implantation, whilst speech outcomes following GPI-DBS have received far less attention. Research comparing speech outcomes for patients with PD receiving STN-DBS and GPI-DBS can inform pre-surgical counseling and assist with clinician and patient decision-making when considering the neural targets selected for DBS-implantation. The aims of this pilot study were (1) to compare perceptual and acoustic speech outcomes for a group of patients with PD receiving bilateral DBS in the STN or the GPI with DBS stimulation both ON and OFF, and (2) examine associations between acoustic and perceptual speech measures and clinical characteristics. Methods: Ten individuals with PD receiving STN-DBS and eight individuals receiving GPI-DBS were audio-recorded reading a passage. Three listeners blinded to neural target and stimulation condition provided perceptual judgments of intelligibility and overall speech severity. Speech acoustic measures were obtained from the recordings. Acoustic and perceptual measures and clinical characteristics were compared for the two neural targets and stimulation conditions. Results: Intelligibility and speech severity were not significantly different across neural target or stimulation conditions. Generally, acoustic measures were also not statistically different for the two neural targets or stimulation conditions. Acoustic measures reflecting more varied speech prosody were associated with improved intelligibility and lessened severity. Convergent correlations were found between UPDRS-III speech scores and perceptual measures of intelligibility and severity. Conclusion: This study reports a systematic comparison of perceptual and acoustic speech outcomes following STN-DBS and GPI-DBS. Statistically significant differences in acoustic measures for the two neural targets were small in magnitude and did not yield group differences in perceptual measures. The absence of robust differences in speech outcomes for the two neural targets has implications for pre-surgical counseling. Results provide preliminary support for reliance on considerations other than speech when selecting the target for DBS in patients with PD.

Zaki Brahmi,1 Mohammad Mahyoob,2 Mohammed Al-Sarem,1 Jeehaan Algaraady,3 Khadija Bousselmi,4 Abdulaziz Alblwi1 1Department of Computer Science, Taibah University, Madina, Kingdom of Saudi Arabia; 2Department of Languages and Translation, Taibah University, Madina, Kingdom of Saudi Arabia; 3Department of English, Taiz University, Taiz, Yemen; 4Department of Computer Science, LISTIC, University of Savoie Mont Blanc, Chambéry, FranceCorrespondence: Mohammad Mahyoob, Email mqassem@taibahu.edu.saPurpose: Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.Methods: The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.Originality: This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014– 2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.Findings: The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).Keywords: speech disorder, speech recognition, dysarthria, machine learning, assistive technologies

Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models' performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal's integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process. [ABSTRACT FROM AUTHOR]

Dysarthria is a common and debilitating symptom of many neurodegenerative diseases, including those resulting in ataxia. Changes to speech lead to significant reductions in quality of life, impacting the speaker in most daily activities. Recognition of its importance as an objective outcome measure in clinical trials for ataxia is growing. Its viability as an endpoint across the disease spectrum (i.e. pre-symptomatic onwards) means that trials can recruit ambulant individuals and later-stage individuals who are often excluded because of difficulty completing lower limb tasks. Here we discuss the key considerations for speech testing in clinical trials including hardware selection, suitability of tasks and their role in protocols for trials and propose a core set of tasks for speech testing in clinical trials. Test batteries could include forms suitable for remote short, sensitive and easy to use, with norms available in several languages. The use of artificial intelligence also could improve accuracy and automaticity of analytical pipelines in clinic and trials. [ABSTRACT FROM AUTHOR]

Objective: To identify and agree on what outcome domains should be measured in research and clinical practice when working with stroke survivors who have dysarthria. Design: Delphi process, two rounds of an online survey followed by two online consensus meetings. Setting: UK and Australia. Participants: Stroke survivors with experience of dysarthria, speech and language therapists/pathologists working in stroke and communication researchers. Methods: Initial list of outcome domains generated from existing literature and with our patient and public involvement group to develop the survey. Participants completed two rounds of this survey to rate importance. Outcomes were identified as 'in', 'unclear' or 'out' from the second survey. All participants were invited to two consensus meetings to discuss these results followed by voting to identify critically important outcome domains for a future Core Outcome Set. All outcomes were voted on in the consensus meetings and included if 70% of meeting participants voted 'yes' for critically important. Results: In total, 148 surveys were fully completed, and 28 participants attended the consensus meetings. A core outcome set for dysarthria after stroke should include four outcome domains: (a) intelligibility of speech, (b) ability to participate in conversations, (c) living well with dysarthria, (d) skills and knowledge of communication partners (where relevant). Conclusions: We describe the consensus of 'what' speech outcomes after stroke are valued by all stakeholders including those with lived experience. We share these findings to encourage the measurement of these domains in clinical practice and research and for future research to identify 'how' best to measure these outcomes. [ABSTRACT FROM AUTHOR]

Articulation disorder, as a medical difficulty, currently mainstream speech recognition technologies are not well adapted to the needs of this field. At the same time, speech recognition technology for dysarthria utilizes a combination of pre training and personalized training to further improve algorithm performance and reduce recognition word error rate through data-driven methods. However, currently, speech recognition technology for dysarthria still has a certain distance from practical commercial use, and its development is limited by data scale and technology. So far, there have been no comprehensive articles on speech recognition for dysarthria. It is urgent to compare and analyze the construction methods and advanced technologies of various datasets in this field, in order to facilitate researchers entering the field to quickly acquire knowledge in this field. This paper conducts a survey on existing datasets, mainstream algorithms, and evaluation methods, and summarizes the scale, form, and characteristics of mainstream speech impairment datasets at home and abroad. It analyzes the mainstream algorithms for speech recognition with dysarthria, and provides the performance and characteristics of different algorithms. Finally, the performance evaluation indicators of the algorithm model based on the severity level of patients with dysarthria are studied, and future research directions are discussed, in order to provide help for the researchers engaged in speech recognition with dysarthria and assist in the rapid development of this field. [ABSTRACT FROM AUTHOR]

Primary progressive apraxia of speech (PPAOS) is a neurodegenerative syndrome characterized by the progressive and initially isolated or predominant onset of difficulties in the planning/programming of movements necessary for speech production and can be accompanied by dysarthria. To date, no study has used an evidence-based treatment to address phonation control in patients with PPAOS. The aim of this study was to evaluate the feasibility and efficacy of LSVT LOUD® as a treatment for phonatory control in speakers with PPAOS. Three speakers with PPAOS received LSVT LOUD® therapy, and changes in phonatory control, voice quality and prosody were measured immediately, and one, four and eight weeks after the end of the treatment. Overall, the results suggest that the treatment is feasible and could improve voice quality, intensity, and control in some patients with PPAOS. The generalization of the results is also discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for automated dysarthria detection and severity assessment using a variable short-time Fourier transform layered convolutional neural networks (CNN) model. Dysarthria is a speech disorder characterized by difficulties in articulation, resulting in unclear speech. The model is evaluated on two datasets, TORGO and UA-Speech, consisting of individuals with dysarthria and healthy controls. Various variations of the CNN's first layer, including spectrogram, log spectrogram, and pre-emphasis filtering (PEF) with and without learnables, are investigated. Notably, the PEF with 5 learnables achieves the highest accuracy in detecting dysarthria and assessing its severity. The study highlights the significance of dataset size, with UA-Speech dataset showing superior performance due to its larger size, enabling better capture of dysarthria severity variations. This research contributes to the advancement of objective dysarthria assessment, aiding in early diagnosis and personalized treatment for individuals with speech disorders. [ABSTRACT FROM AUTHOR]

RFC1 disease, caused by biallelic repeat expansion in RFC1 , is clinically heterogeneous in terms of age of onset, disease progression and phenotype. We investigated the role of the repeat size in influencing clinical variables in RFC1 disease. We also assessed the presence and role of meiotic and somatic instability of the repeat. In this study, we identified 553 patients carrying biallelic RFC1 expansions and measured the repeat expansion size in 392 cases. Pearson's coefficient was calculated to assess the correlation between the repeat size and age at disease onset. A Cox model with robust cluster standard errors was adopted to describe the effect of repeat size on age at disease onset, on age at onset of each individual symptoms, and on disease progression. A quasi-Poisson regression model was used to analyse the relationship between phenotype and repeat size. We performed multivariate linear regression to assess the association of the repeat size with the degree of cerebellar atrophy. Meiotic stability was assessed by Southern blotting on first-degree relatives of 27 probands. Finally, somatic instability was investigated by optical genome mapping on cerebellar and frontal cortex and unaffected peripheral tissue from four post-mortem cases. A larger repeat size of both smaller and larger allele was associated with an earlier age at neurological onset [smaller allele hazard ratio (HR) = 2.06, P < 0.001; larger allele HR = 1.53, P < 0.001] and with a higher hazard of developing disabling symptoms, such as dysarthria or dysphagia (smaller allele HR = 3.40, P < 0.001; larger allele HR = 1.71, P = 0.002) or loss of independent walking (smaller allele HR = 2.78, P < 0.001; larger allele HR = 1.60; P < 0.001) earlier in disease course. Patients with more complex phenotypes carried larger expansions [smaller allele: complex neuropathy rate ratio (RR) = 1.30, P = 0.003; cerebellar ataxia, neuropathy and vestibular areflexia syndrome (CANVAS) RR = 1.34, P < 0.001; larger allele: complex neuropathy RR = 1.33, P = 0.008; CANVAS RR = 1.31, P = 0.009]. Furthermore, larger repeat expansions in the smaller allele were associated with more pronounced cerebellar vermis atrophy (lobules I–V β = −1.06, P < 0.001; lobules VI–VII β = −0.34, P = 0.005). The repeat did not show significant instability during vertical transmission and across different tissues and brain regions. RFC1 repeat size, particularly of the smaller allele, is one of the determinants of variability in RFC1 disease and represents a key prognostic factor to predict disease onset, phenotype and severity. Assessing the repeat size is warranted as part of the diagnostic test for RFC1 expansion. [ABSTRACT FROM AUTHOR]

Objective: Language dysfunction is one of the most common cognitive impairments in amyotrophic lateral sclerosis (ALS). Although discourse capacities are essential for daily functioning, verbal expressive language has not been widely investigated in ALS. The existing research available suggests that discourse impairments are prevalent. This study investigates verbal expressive language in people living with ALS (plwALS) in contrast to healthy controls (HC). Methods: 64 plwALS and 49 age, gender and education-matched healthy controls were ask to describe the Cookie Theft Picture Task. The recordings were analyzed for discourse productivity, discourse content, syntactic complexity, speech fluency and verb processing. We applied the Bayesian hypothesis-testing framework, incorporating the effects of dysarthria, cognitive impairment status (CIS), and premorbid crystalline verbal IQ. Results: Compared to HC, plwALS only showed a single impairment: speech dysfluency. Discourse productivity, discourse content, syntactic complexity and verb processing were not impaired. Cognition and dysarthria exceeded the influence of verbal IQ for total words spoken and content density. Cognition alone seemed to explain dysfluency. Body-agent verbs were produced at even higher rates than other verb types. For the remaining outcomes, verbal IQ was the most decisive factor. Conclusions: In contrast to existing research, our data demonstrates no discernible impairment in verbal expressive language in ALS. What our findings show to be decisive is accounting for the influence of dysarthria, cognitive impairment status, and verbal IQ as variables on spontaneous verbal expressive language. Minor impairments in verbal expressive language appear to be influenced to a greater degree by executive dysfunctioning and dysarthria than by language impairment. [ABSTRACT FROM AUTHOR]

To evaluate automated digital speech measures, derived from spontaneous speech (picture descriptions), in assessing bulbar motor impairments in patients with ALS-FTD spectrum disorders (ALS-FTSD). Automated vowel algorithms were employed to extract two vowel acoustic measures: vowel space area (VSA), and mean second formant slope (F2 slope). Vowel measures were compared between ALS with and without clinical bulbar symptoms (ALS + bulbar (n = 49, ALSFRS-r bulbar subscore: x¯ = 9.8 (SD = 1.7)) vs. ALS-nonbulbar (n = 23), behavioral variant frontotemporal dementia (bvFTD, n = 25) without a motor syndrome, and healthy controls (HC, n = 32). Correlations with bulbar motor clinical scales, perceived listener effort, and MRI cortical thickness of the orobuccal primary motor cortex (oral PMC) were examined. We compared vowel measures to speaking rate, a conventional metric for assessing bulbar dysfunction. ALS + bulbar had significantly reduced VSA and F2 slope than ALS-nonbulbar (|d|=0.94 and |d|=1.04, respectively), bvFTD (|d|=0.89 and |d|=1.47), and HC (|d|=0.73 and |d|=0.99). These reductions correlated with worse bulbar clinical scores (VSA: R = 0.33, p = 0.043; F2 slope: R = 0.38, p = 0.011), greater listener effort (VSA: R=-0.43, p = 0.041; F2 slope: p > 0.05), and cortical thinning in oral PMC (F2 slope: β = 0.0026, p = 0.017). Vowel measures demonstrated greater sensitivity and specificity for bulbar impairment than speaking rate, while showing independence from cognitive and respiratory impairments. Automatic vowel measures are easily derived from a brief spontaneous speech sample, are sensitive to mild-moderate stage of bulbar disease in ALS-FTSD, and may present better sensitivity to bulbar impairment compared to traditional assessments such as speaking rate. [ABSTRACT FROM AUTHOR]

Introduction: This study examined the utility of multiple second formant (F2) slope metrics to capture differences in speech production for individuals with dysarthria and healthy controls as a function of speaking rate. In addition, the utility of F2 slope metrics for predicting severity of intelligibility impairment in dysarthria was examined. Methods: Twenty three speakers with Parkinson's disease and mild to moderate hypokinetic dysarthria (HD), 9 speakers with various neurological diseases and mild to severe ataxic or ataxic-spastic dysarthria (AD), and 26 age-matched healthy control speakers (CON) participated in a sentence repetition task. Sentences were produced at habitual, fast, and slow speaking rate. A variety of metrics were derived from the rising F2 transition portion of the diphthong /ai/. To obtain measures of intelligibility for the two clinical speaker groups, 15 undergraduate SLP students participated in a transcription experiment. Results: Significantly shallower slopes were found for the speakers with HD compared to control speakers. Steeper F2 slopes were associated with increased speaking rate for all groups. Higher variability in F2 slope metrics was found for the speakers with AD compared to the two other speaker groups. For both clinical speaker groups, there was a negative association between intelligibility and F2 slope variability metrics, indicating lower variability in speech production was associated with higher intelligibility. Discussion: F2 slope metrics were sensitive to dysarthria presence, dysarthria type, and speaking rate. The current study provided evidence that the use of F2 slope variability measures has additional value to F2 slope averaged measures for predicting severity of intelligibility impairment in dysarthria. [ABSTRACT FROM AUTHOR]

Purpose: Changes in voice and speech are characteristic symptoms of Huntington’s disease (HD). Objective methods for quantifying speech impairment that can be used across languages could facilitate assessment of disease progression and intervention strategies. The aim of this study was to analyze acoustic features to identify language-independent features that could be used to quantify speech dysfunction in English-, Spanish-, and Polish-speaking participants with HD. Method: Ninety participants with HD and 83 control participants performed sustained vowel, syllable repetition, and reading passage tasks recorded with previously validated methods using mobile devices. Language-independent features that differed between HD and controls were identified. Principal component analysis (PCA) and unsupervised clustering were applied to the language-independent features of the HD data set to identify subgroups within the HD data. Results: Forty-six language-independent acoustic features that were significantly different between control participants and participants with HD were identified. Following dimensionality reduction using PCA, four speech clusters were identified in the HD data set. Unified Huntington’s Disease Rating Scale (UHDRS) total motor score, total functional capacity, and composite UHDRS were significantly different for pairwise comparisons of subgroups. The percentage of HD participants with higher dysarthria score and disease stage also increased across clusters. Conclusion: The results support the application of acoustic features to objectively quantify speech impairment and disease severity in HD in multilanguage studies. [ABSTRACT FROM AUTHOR]