Severity Of Dysarthria Research Articles

Effectiveness of remotely delivered speech therapy in persons with Parkinson's disease – a randomised controlled trial

Increasing evidence supports the merits of speech therapy in Parkinson's disease, but the current practice of multiple in-house treatments is demanding for patients. We therefore assessed the effectiveness of remotely delivered and personalised speech therapy on improving quality of life and speech quality in persons with Parkinson's disease. We performed a single blinded randomised controlled trial (the PERSPECTIVE study), comparing 8 weeks of personalised remote speech therapy to no intervention (waiting list design). Patients with reduced speech intelligibility were included, regardless of disease stage or dysarthria severity. Patients were assigned randomly (1:1) to the intervention or control group. Measurements took place at baseline and after 8 weeks (both groups), and after 32 weeks (intervention group only). Patients were treated remotely by 20 experienced speech therapists. The primary outcome was disease-related quality of life at 8 weeks, assessed with the Parkinson's Disease Questionnaire 39 (PDQ-39). Data were analysed using analysis of covariance based on the intention-to-treat principle. This trial is registered in ClinicalTrials.gov, NCT03963388. Between March 1, 2019, and March 27, 2021, 214 patients were enrolled in the intervention group (n=109) or control group (n=105). At the primary timepoint, the adjusted mean difference in PDQ-39 was-2.0 in favour of the intervention group (95% CI-4.0 to 0.1); p=0.056). The intervention group scored better on the communication index score of the PDQ-39 (post hoc analysis), with an adjusted mean difference of-5.3 (95% CI-9.4 to-1.2; p=0.011). We found no between-group differences on any other PDQ-39 domain. Follow-up measurements showed a significant reduction of the PDQ-39 compared to the primary timepoint with a difference of 2.40 (95%CI 0.77-4.02; p=0.004). Personalised remote speech therapy improved communication-related quality of life, but not overall quality of life. Michael J. Fox Foundation, Gatsby Foundation, and Health∼Holland.

Accurate synthesis of dysarthric Speech for ASR data augmentation

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech Recognition (ASR) systems can help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech, which is not readily available for dysarthric talkers.This paper presents a new dysarthric speech synthesis method for the purpose of ASR training data augmentation. Differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels are important components for dysarthric speech modeling, synthesis, and augmentation. For dysarthric speech synthesis, a modified neural multi-talker TTS is implemented by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels.To evaluate the effectiveness for synthesis of training data for ASR, dysarthria-specific speech recognition was used. Results show that a DNNHMM model trained on additional synthetic dysarthric speech achieves relative Word Error Rate (WER) improvement of 12.2 % compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5 %, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has significant impact on the dysarthric ASR systems. In addition, we have conducted a subjective evaluation to evaluate the dysarthricness and similarity of synthesized speech. Our subjective evaluation shows that the perceived dysarthricness of synthesized speech is similar to that of true dysarthric speech, especially for higher levels of dysarthria. Audio samples are available at https://mohammadelc.github.io/SpeechGroupUKY/

Voice signals database of ALS patients with different dysarthria severity and healthy controls

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.

Automatic dysarthria detection and severity level assessment using CWT-layered CNN model

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.

Dysarthria and dysphagia in patients with mitochondrial diseases

BackgroundInformation about dysarthria and dysphagia in mitochondrial diseases (MD) is scarce. However, this knowledge is needed to identify speech and swallowing problems early, to monitor the disease course, and to develop and offer optimal treatment and support. This study therefore aims to examine the prevalence and severity of dysarthria and dysphagia in patients with MD and its relation to clinical phenotype and disease severity. Secondary aim is to determine clinically relevant outcome measures for natural history studies and clinical trials. MethodsThis retrospective cross-sectional medical record study includes adults (age ≥ 18 years) diagnosed with genetically confirmed MD who participated in a multidisciplinary admission within the Radboud center for mitochondrial medicine between January 2015 and April 2023. Dysarthria and dysphagia were examined by administering the Radboud dysarthria assessment, swallowing speed, dysphagia limit, test of mastication and swallowing solids (TOMASS), and 6-min mastication test (6MMT). The disease severity was assessed using the Newcastle mitochondrial disease scale for adults (NMDAS). ResultsThe study included 224 patients with MD with a median age of 42 years of whom 37.5% were male. The pooled prevalence of dysarthria was 33.8% and of dysphagia 35%. Patients with MD showed a negative deviation from the norm on swallowing speed, TOMASS (total time) and the 6MMT. Furthermore, a significant moderate relation was found between the presence of dysarthria and the clinical phenotypes. There was a statistically significant difference in total time on the TOMASS between the clinical phenotypes. Finally, disease severity showed a significant moderate relation with the severity of dysarthria and a significant weak relation with the severity of dysphagia. ConclusionDysarthria and dysphagia occur in about one-third of patients with MD. It is important for treating physicians to pay attention to this subject because of the influence of both disorders on social participation and wellbeing. Referral to a speech and language therapist should therefore be considered, especially in patients with a more severe clinical phenotype. The swallowing speed, TOMASS and 6MMT are the most clinically relevant tests to administer.

Listener effort quantifies clinically meaningful progression of dysarthria in people living with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative motor neuron disease that causes progressive muscle weakness. Progressive bulbar dysfunction causes dysarthria and thus social isolation, reducing quality of life. The Everything ALS Speech Study obtained longitudinal clinical information and speech recordings from 292 participants. In a subset of 120 participants, we measured speaking rate (SR) and listener effort (LE), a measure of dysarthria severity rated by speech pathologists from recordings. LE intra- and inter-rater reliability was very high (ICC 0.88 to 0.92). LE correlated with other measures of dysarthria at baseline. LE changed over time in participants with ALS (slope 0.77 pts/month; p<0.001) but not controls (slope 0.005 pts/month; p=0.807). The slope of LE progression was similar in all participants with ALS who had bulbar dysfunction at baseline, regardless of ALS site of onset. LE could be a remotely collected clinically meaningful clinical outcome assessment for ALS clinical trials.

Smartphone-Based Speech Therapy for Poststroke Dysarthria: Pilot Randomized Controlled Trial Evaluating Efficacy and Feasibility.

Dysarthria is a common poststroke speech disorder affecting communication and psychological well-being. Traditional speech therapy is effective but often poses challenges in terms of accessibility and patient adherence. Emerging smartphone-based therapies may offer promising alternatives for the treatment of poststroke dysarthria. This study aimed to assess the efficacy and feasibility of smartphone-based speech therapy for improving speech intelligibility in patients with acute and early subacute poststroke dysarthria. This study also explored the impact of the intervention on psychological well-being, user experience, and overall feasibility in a clinical setting. Participants were divided into 2 groups for this randomized, evaluator-blinded trial. The intervention group used a smartphone-based speech therapy app for 1 hour per day, 5 days per week, for 4 weeks, with guideline-based standard stroke care. The control group received standard guideline-based stroke care and rehabilitation. Speech intelligibility, psychological well-being, quality of life, and user acceptance were assessed using repeated measures ANOVA. In this study, 40 patients with poststroke dysarthria were enrolled, 32 of whom completed the trial (16 in each group). The intervention group showed significant improvements in speech intelligibility compared with the control group. This was evidenced by improvements from baseline (F1,30=34.35; P<.001), between-group differences (F1,30=6.18; P=.02), and notable time-by-group interactions (F1,30=6.91; P=.01). Regarding secondary outcomes, the intervention led to improvements in the percentage of correct consonants over time (F1,30=5.57; P=.03). In addition, significant reductions were noted in the severity of dysarthria in the intervention group over time (F1,30=21.18; P<.001), with a pronounced group effect (F1,30=5.52; P=.03) and time-by-group interaction (F1,30=5.29; P=.03). Regarding quality of life, significant improvements were observed as measured by the EQ-5D-3L questionnaire (F1,30=13.25; P<.001) and EQ-VAS (F1,30=7.74; P=.009) over time. The adherence rate to the smartphone-based app was 64%, with over half of the participants completing all the sessions. The usability of the app was rated high (system usability score 80.78). In addition, the intervention group reported increased self-efficacy in using the app compared with the control group (F1,30=10.81; P=.003). The smartphone-based speech therapy app significantly improved speech intelligibility, articulation, and quality of life in patients with poststroke dysarthria. These findings indicate that smartphone-based speech therapy can be a useful assistant device in the management of poststroke dysarthria, particularly in the acute and early subacute stroke stages. ClinicalTrials.gov NCT05146765; https://clinicaltrials.gov/ct2/show/NCT05146765.

Clinical Insights Into the Use of Speech Amplification Devices for Managing Hypophonia: Interviews With Speech-Language Pathologists.

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). 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. 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. 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.

Contribution of frontal right-hemispheric speech tracts and the superior frontal gyrus to the severity of aphasia and dysarthria in brain tumor patients

Contribution of frontal right-hemispheric speech tracts and the superior frontal gyrus to the severity of aphasia and dysarthria in brain tumor patients

Clinical dimensions along the non-fluent variant primary progressive aphasia spectrum.

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.

Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motorneurons of the bulbar, cervical, thoracic, or lumbar segments. Bulbar presentation is a devastating characteristic that impairs patients’ ability to communicate and is linked to shorter survival. Early acoustic manifestation of voice symptoms, such as dysarthria, is very variable, making its detection and classification challenging, both by human specialists and automatic systems. In this context, precision medicine, defined as “prevention and treatment strategies that take individual variability into account”, has gained a great interest in the ALS community. Specifically, the use of innovative Artificial Intelligence techniques, such as Machine Learning, plays a pivotal role in finding specific patterns in the data set to help neurologists in clinical decision-making. Therefore, the main objective of this study was to find new markers, and new patterns, to promptly detect the possible presence of dysarthria and to correctly classify its severity. We have performed an acoustic analysis on different voice signals of various degrees of impairment acquired during outpatient visits at the ALS center of the “Federico II” University Hospital. From the collected signals, a new database containing different acoustic parameters was realized, on which several experiments were performed. The study led us to the discovery of markers that helped to develop a decision tree that separated healthy subjects from patients and, among patients, those with different severity of dysarthria. This model achieved good results in terms of dysarthria classification accuracy, 86.6%, which is excellent considering the small number of subjects in the data set.

Representation learning strategies to model pathological speech: Effect of multiple spectral resolutions

This paper considers a representation learning strategy to model speech signals from patients with Parkinson’s disease, with the goal of predicting the presence of the disease, and evaluating the level of degradation of a patient’s speech. In particular, we propose a novel fusion strategy that combines wideband and narrowband spectral resolutions using a representation learning strategy based on autoencoders, called the multi-spectral autoencoder. The proposed model is able to classify the speech from Parkinson’s disease patients with accuracy up to 97%. The proposed model is also able to assess the dysarthria severity of Parkinson’s disease patients with a Spearman correlation up to 0.79. These results outperform those observed in literature where the same problem was addressed with the same corpus.

Sex Differences in the Speech of Persons With and Without Parkinson's Disease.

Sex differences are apparent in the prevalence and the clinical presentation of Parkinson's disease (PD), but their effects on speech have been less studied. Speech acoustics of persons with (34 females and 34 males) and without (age- and sex-matched) PD were examined, assessing the effects of PD diagnosis and sex on ratings of dysarthria severity and acoustic measures of phonation (fundamental frequency standard deviation, smoothed cepstral peak prominence), speech rate (net syllables per second, percent pause ratio), and articulation (articulatory-acoustic vowel space, release burst precision). Most measures were affected by PD (dysarthria severity, fundamental frequency standard deviation) and sex (smoothed cepstral peak prominence, net syllables per second, percent pause ratio, articulatory-acoustic vowel space), but without interactions between them. Release burst precision was differentially affected by sex in PD. Relative to those without PD, persons with PD produced fewer plosives with a single burst: females more frequently produced multiple bursts, whereas males more frequently produced no burst at all. Most metrics did not indicate that speech production is differentially affected by sex in PD. Sex was, however, associated with disparate effects on release burst precision in PD, which deserves further study. https://doi.org/10.23641/asha.24388666.

A large-scale comparison of two voice synthesis techniques on intelligibility, naturalness, preferences, and attitudes toward voices banked by individuals with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) commonly results in the inability to produce natural speech, making speech-generating devices (SGDs) important. Historically, synthetic voices generated by SGDs were neither unique, nor age- or dialect-appropriate, which depersonalized SGD use. Voices generated by SGDs can now be customized via voice banking and should ideally sound uniquely like the individual’s natural speech, be intelligible, and elicit positive reactions from communication partners. This large-scale 2 x 2 mixed between- and within-participants design examined perceptions of 831 adult listeners regarding custom synthetic voices created for two individuals diagnosed with ALS via two synthesis systems in common clinical use (waveform concatenation and statistical parametric synthesis). The study explored relationships among synthesis system, dysarthria severity, synthetic speech intelligibility, naturalness, and preferences, and also provided a preliminary examination of attitudes regarding the custom synthetic voices. Synthetic voices generated via statistical parametric synthesis trained on deep neural networks were more intelligible, natural, and preferred than voices produced via waveform concatenation, and were associated with more positive attitudes. The custom synthetic voice created from moderately dysarthric speech was more intelligible than the voice created from mildly dysarthric speech. Clinical implications and factors that may have contributed to the relative intelligibilities are discussed.

Neural basis of speech and grammar symptoms in non-fluent variant primary progressive aphasia spectrum.

The non-fluent/agrammatic variant of primary progressive aphasia (nfvPPA) is a neurodegenerative syndrome primarily defined by the presence of apraxia of speech (AoS) and/or expressive agrammatism. In addition, many patients exhibit dysarthria and/or receptive agrammatism. This leads to substantial phenotypic variation within the speech-language domain across individuals and time, in terms of both the specific combination of symptoms as well as their severity. How to resolve such phenotypic heterogeneity in nfvPPA is a matter of debate. 'Splitting' views propose separate clinical entities: 'primary progressive apraxia of speech' when AoS occurs in the absence of expressive agrammatism, 'progressive agrammatic aphasia' (PAA) in the opposite case, and 'AOS + PAA' when mixed motor speech and language symptoms are clearly present. While therapeutic interventions typically vary depending on the predominant symptom (e.g. AoS versus expressive agrammatism), the existence of behavioural, anatomical and pathological overlap across these phenotypes argues against drawing such clear-cut boundaries. In the current study, we contribute to this debate by mapping behaviour to brain in a large, prospective cohort of well characterized patients with nfvPPA (n = 104). We sought to advance scientific understanding of nfvPPA and the neural basis of speech-language by uncovering where in the brain the degree of MRI-based atrophy is associated with inter-patient variability in the presence and severity of AoS, dysarthria, expressive agrammatism or receptive agrammatism. Our cross-sectional examination of brain-behaviour relationships revealed three main observations. First, we found that the neural correlates of AoS and expressive agrammatism in nfvPPA lie side by side in the left posterior inferior frontal lobe, explaining their behavioural dissociation/association in previous reports. Second, we identified a 'left-right' and 'ventral-dorsal' neuroanatomical distinction between AoS versus dysarthria, highlighting (i) that dysarthria, but not AoS, is significantly influenced by tissue loss in right-hemisphere motor-speech regions; and (ii) that, within the left hemisphere, dysarthria and AoS map onto dorsally versus ventrally located motor-speech regions, respectively. Third, we confirmed that, within the large-scale grammar network, left frontal tissue loss is preferentially involved in expressive agrammatism and left temporal tissue loss in receptive agrammatism. Our findings thus contribute to define the function and location of the epicentres within the large-scale neural networks vulnerable to neurodegenerative changes in nfvPPA. We propose that nfvPPA be redefined as an umbrella term subsuming a spectrum of speech and/or language phenotypes that are closely linked by the underlying neuroanatomy and neuropathology.

Optimising verbal fluency analysis in neurological patients with dysarthria: examples from Parkinson’s disease and hereditary ataxia

ABSTRACT Background Verbal fluency tests (VFTs) are widely used to assess cognitive-linguistic performance in neurological diseases. However, the influence of dysarthria on performance in tests requiring oral responses is unclear in ataxia and Parkinson’s disease. Objectives To determine the impact of dysarthria on VFT performance and evaluate the validity and reliability of alternative methods for analyzing VFT data. Method Trained raters evaluated dysarthria using VFT recordings in people with ataxia (N = 61) or Parkinson’s disease (PD; N = 69). Total Correct Items scores and qualitative parameters (intrusions, ambiguous verbalizations, perseverations, and interjections) were compared across semantic, phonemic, and alternating fluency tasks. Disease severity was considered as a covariate in the regression model. Results VFT dysarthria ratings correlated with the benchmark (ground truth) dysarthria scores derived from a monologue. Ambiguous responses resulting from unclear speech impeded the rater’s ability to determine if a response was correct. Regression analysis indicated that more severe dysarthria ratings predicted diminished scores in all three tasks (semantic fluency, phonemic fluency and alternating fluency) in the ataxia group. The contribution of disease severity to semantic, phonemic and alternating fluency was reduced substantially in the ataxia group after accounting for dysarthria severity in the model in both groups. Conclusions Dysarthria severity can be estimated based on speech samples derived from VFT. Dysarthria can lead to lower total correct items and is associated with more ambiguous verbalizations in VFT. Dysarthria severity should be considered when interpreting VFT performance in common movement disorders.

Automated Vowel Articulation Analysis in Connected Speech Among Progressive Neurological Diseases, Dysarthria Types, and Dysarthria Severities.

Although articulatory impairment represents distinct speech characteristics in most neurological diseases affecting movement, methods allowing automated assessments of articulation deficits from the connected speech are scarce. This study aimed to design a fully automated method for analyzing dysarthria-related vowel articulation impairment and estimate its sensitivity in a broad range of neurological diseases and various types and severities of dysarthria. Unconstrained monologue and reading passages were acquired from 459 speakers, including 306 healthy controls and 153 neurological patients. The algorithm utilized a formant tracker in combination with a phoneme recognizer and subsequent signal processing analysis. Articulatory undershoot of vowels was presented in a broad spectrum of progressive neurodegenerative diseases, including Parkinson's disease, progressive supranuclear palsy, multiple-system atrophy, Huntington's disease, essential tremor, cerebellar ataxia, multiple sclerosis, and amyotrophic lateral sclerosis, as well as in related dysarthria subtypes including hypokinetic, hyperkinetic, ataxic, spastic, flaccid, and their mixed variants. Formant ratios showed a higher sensitivity to vowel deficits than vowel space area. First formants of corner vowels were significantly lower for multiple-system atrophy than cerebellar ataxia. Second formants of vowels /a/ and /i/ were lower in ataxic compared to spastic dysarthria. Discriminant analysis showed a classification score of up to 41.0% for disease type, 39.3% for dysarthria type, and 49.2% for dysarthria severity. Algorithm accuracy reached an F-score of 0.77. Distinctive vowel articulation alterations reflect underlying pathophysiology in neurological diseases. Objective acoustic analysis of vowel articulation has the potential to provide a universal method to screen motor speech disorders. https://doi.org/10.23641/asha.23681529.

Artificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment

Dysarthria is common in movement disorders, such as Wilson's disease (WD), Parkinson's disease, or Huntington's disease. Dysarthria severity assessment is often indispensable for the management of these diseases. However, such assessment is usually labor‐intensive, time‐consuming, and expensive. To seek efficient and cost‐effective solutions for dysarthria assessment, an artificial intelligence (AI)‐powered acoustic analysis system is proposed and its performance in a valuable sample of WD, an ideal disease model with mainly mixed dysarthria, is verified. A test‐retest reliability analysis yields excellent reproducibility in the acoustic measures (mean intraclass correlation coefficient [ICC] = 0.81). Then, a system for dysarthria assessment is trained with WD patients (n = 65) and sex‐matched healthy controls (n = 65) using a machine learning approach. The system achieves reasonable performance in evaluating dysarthria severity with either stepwise classification or regression (all areas under the curve &gt;80%; mean absolute error = 6.25, r = 0.79, p &lt; 0.0001). The diadochokinesis and sustained phonation tasks contribute the most to prediction, and the corresponding acoustic features can provide significant and independent contributions. The present study demonstrates the feasibility and good performance of the AI‐powered acoustic analysis framework, offering the potential to facilitate early screening and subsequent management of dysarthria.

Dysarthria severity assessment using squeeze-and-excitation networks

Automated dysarthria severity identification can aid clinicians in monitoring the patient’s progress, and can improve the performance of dysarthric speech recognition systems. In this paper, we explore the potency of squeeze-and-excitation (SE) networks for dysarthria severity level classification using mel spectrograms. Deep convolutional neural network (CNN) models built using SE components and residual blocks are analysed in this regard. A comparison of the system performance is done against a shallow CNN and a convolutional recurrent neural network built using a bidirectional long short-term memory network. The models are evaluated on the Universal Access dysarthric speech corpus under speaker-dependent (SD) and speaker-independent (SID) scenarios. The proposed model obtained appreciable results in the SD scenario, and its margin of about 10% over the baselines in the SID scenario promises its applicability in automated dysarthria severity level classification.

The Apraxia of Speech Rating Scale: Reliability, Validity, and Utility.

The purpose of this study was to examine the interrater reliability and validity of the Apraxia of Speech Rating Scale (ASRS-3.5) as an index of the presence and severity of apraxia of speech (AOS) and the prominence of several of its important features. Interrater reliability was assessed for 27 participants. Validity was examined in a cohort of 308 participants (120 with and 188 without progressive AOS) through item analysis; item-Total score correlations; correlations among ASRS Total score and component subscores and independent clinical ratings of AOS, dysarthria and aphasia severity, intelligibility, and articulatory errors, as well as years postonset and age; and regression models assessing item and Total score prediction of AOS presence. Interrater reliability was good or excellent for most items and excellent for the Total score. Item and Total score analyses revealed good separation of participants with versus without AOS. Inter-item and item-Total score correlations were generally moderately high as were correlations between the ASRS Total score and independent ratings of AOS severity, intelligibility, and articulatory errors. The Total score was not meaningfully correlated with ratings of aphasia and dysarthria severity, years postonset, or age. Total scores below 7 and above 10 revealed excellent diagnostic sensitivity and specificity for AOS. The presence of eight or more abnormal features was also highly predictive of AOS presence. The ASRS-3.5 is a reliable and valid scale for identifying the presence and severity of AOS and its predominant features. It has excellent sensitivity to AOS presence and excellent specificity relative to aphasia and dysarthria in patients with neurodegenerative disease. https://doi.org/10.23641/asha.21817584.