Acoustic analysis of voice in bulbar amyotrophic lateral sclerosis: a systematic review and meta-analysis of studies

Voice as a Biomarker in Health-Tech: Mapping the Evolving Landscape of Voice Biomarkers in the Start-Up World

OBJECTIVE

The vocal biomarkers market was worth $1.9B in 2021 and is projected to exceed $5.1B by 2028, for a compound annual growth rate of 15.15%. The investment growth demonstrates a blossoming interest in voice and artificial intelligence (AI) as it relates to human health. The objective of this study was to map the current landscape of start-ups utilizing voice as a biomarker in health-tech.

DATA SOURCES

A comprehensive search for start-ups was conducted using Google, LinkedIn, Twitter, and Facebook. A review of the research was performed using company website, PubMed, and Google Scholar.

REVIEW METHODS

A 3-pronged approach was taken to thoroughly map the landscape. First, an internet search was conducted to identify current start-ups focusing on products relating to voice as a biomarker of health. Second, Crunchbase was utilized to collect financial and organizational information. Third, a review of the literature was conducted to analyze publications associated with the identified start-ups.

RESULTS

A total of 27 start-up start-ups with a focus in the utilization of AI for developing biomarkers of health from the human voice were identified. Twenty-four of these start-ups garnered $178,808,039 in investments. The 27 start-ups published 194 publications combined, 128 (66%) of which were peer reviewed.

CONCLUSION

There is growing enthusiasm surrounding voice as a biomarker in health-tech. Academic drive may complement commercialization to best achieve progress in this arena. More research is needed to accurately capture the entirety of the field, including larger industry players, academic institutions, and non-English content.

Chronic respiratory failure negatively affects speech function in patients with bulbar and spinal onset amyotrophic lateral sclerosis: retrospective data from a tertiary referral center

Background: Although dysarthria and respiratory failure are widely described in literature as part of the natural history of Amyotrophic lateral sclerosis (ALS), the specific interaction between them has been little explored.Aim: To investigate the relationship between chronic respiratory failure and the speech of ALS patients.Materials and methods: In this cross-sectional retrospective study we reviewed the medical records of all patients diagnosed with ALS that were accompanied by a tertiary referral center. In order to determine the presence and degree of speech impairment, the Amyotrophic Lateral Sclerosis Functional Rating Scale-revised (ALSFRS-R) speech sub-scale was used. Respiratory function was assessed through spirometry and through venous blood gasometry obtained from a morning peripheral venous sample. To determine whether differences among groups classified by speech function were significant, maximum and mean spirometry values of participants were compared using multivariate analysis of variance (MANOVA) with Tukey's post hoc test.Results: Seventy-five cases were selected, of which 73.3% presented speech impairment and 70.7% respiratory impairment. Respiratory and speech functions were moderately correlated (seated FVC r = 0.64; supine FVC r = 0.60; seated FEV1 r = 0.59 and supine FEV1 r = 0.54, p < .001). Multivariable logistic regression revealed that the following variables were significantly associated with the presence of speech impairment after adjusting for other risk factors: seated FVC (odds ratio [OR] = 0.862) and seated FEV1 (OR = 1.106). The final model was 81.1% predictive of speech impairment. The presence of daytime hypercapnia was not correlated to increasing speech impairment.Conclusion: The restrictive pattern developed by ALS patients negatively influences speech function. Speech is a complex and multifactorial process, and lung volume presents a pivotal role in its function. Thus, we were able to find that lung volumes presented a significant correlation to speech function, especially in those with bulbar onset and respiratory impairment. Neurobiological and physiological aspects of this relationship should be explored in further studies with the ALS population.

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

INTRODUCTION

Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.

OBJECTIVE

The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.

METHODS

A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.

RESULTS

Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).

CONCLUSION

To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.

LEVEL OF EVIDENCE

5 Laryngoscope, 134:1333-1339, 2024.

Vocal tasks for acoustic and/or auditory perceptual analysis for discriminating individuals with and without voice disorders: a systematic review protocol

OBJECTIVE

The primary objective of the present systematic review is to: (1) identify the current vocal tasks being used for acoustic and/or auditory perceptual analysis to differentiate between individuals with and without voice disorders. The secondary objectives are to: (2) evaluate the evidence of the sensitivity, specificity and accuracy of those vocal tasks for acoustic and/or auditory perceptual analysis in discriminating the individuals with voice disorders from those without; and (3) compare the values between the vocal tasks in discriminating individuals with voice disorders from those without.

METHOD AND ANALYSIS

We search the following electronic databases: MEDLINE, EMBASE, CINAHL, Scopus, Web of Science Core Collection, PubMed Central and Google Scholar. Grey literature searches will include ProQuest Dissertations and Theses, ClinicalTrials.gov and the Cochrane Register of Controlled Trials. Websites of professional organisations and textbooks will be hand searched for relevant information related to the research question. Study screening, selection and data extraction will be conducted independently by two reviewers. Any disagreements will be resolved by discussion or by involving a third reviewer.The methodological quality of the included studies will be appraised using the relevant Critical Appraisal Tools by JBI. The clinical guidelines and recommendations for voice assessment by professional bodies will be appraised using the Reporting Items for practice Guidelines in HealThcare (RIGHT) checklist. The findings will be presented in the form of an information matrix with the tasks identified tabulated against the nature of the task, dimensions being tested, and their accuracy, sensitivity, and specificity in identifying individuals with voice problems.

ETHICS AND DISSEMINATION

Formal ethics approval is not required. The findings will be presented at national and international conferences and published in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42023431634.

Subclinical articulatory changes of vowel parameters in Korean amyotrophic lateral sclerosis patients with perceptually normal voices

The available quantitative methods for evaluating bulbar dysfunction in patients with amyotrophic lateral sclerosis (ALS) are limited. We aimed to characterize vowel properties in Korean ALS patients, investigate associations between vowel parameters and clinical features of ALS, and analyze subclinical articulatory changes of vowel parameters in those with perceptually normal voices. Forty-three patients with ALS (27 with dysarthria and 16 without dysarthria) and 20 healthy controls were prospectively collected in the study. Dysarthria was assessed using the ALS Functional Rating Scale-Revised (ALSFRS-R) speech subscores, with any loss of 4 points indicating the presence of dysarthria. The structured speech samples were recorded and analyzed using Praat software. For three corner vowels (/a/, /i/, and /u/), data on the vowel duration, fundamental frequency, frequencies of the first two formants (F1 and F2), harmonics-to-noise ratio, vowel space area (VSA), and vowel articulation index (VAI) were extracted from the speech samples. Corner vowel durations were significantly longer in ALS patients with dysarthria than in healthy controls. The F1 frequency of /a/, F2 frequencies of /i/ and /u/, the VSA, and the VAI showed significant differences between ALS patients with dysarthria and healthy controls. The area under the curve (AUC) was 0.912. The F1 frequency of /a/ and the VSA were the major determinants for differentiating ALS patients who had not yet developed apparent dysarthria from healthy controls (AUC 0.887). In linear regression analyses, as the ALSFRS-R speech subscore decreased, both the VSA and VAI were reduced. In contrast, vowel durations were found to be rather prolonged. The analyses of vowel parameters provided a useful metric correlated with disease severity for detecting subclinical bulbar dysfunction in ALS patients.

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes

Approximately 80-96% of people with amyotrophic lateral sclerosis (ALS) become unable to speak during the disease progression. Assessing upper and lower motor neuron impairment in bulbar regions of ALS patients remains challenging, particularly in distinguishing spastic and flaccid dysarthria. This study aimed to evaluate acoustic voice parameters as useful biomarkers to discriminate ALS clinical phenotypes. Triangular vowel space area (tVSA), alternating motion rates (AMRs), and sequential motion rates (SMRs) were analyzed in 36 ALS patients and 20 sex/age-matched healthy controls (HCs). tVSA, AMR, and SMR values significantly differed between ALS and HCs, and between ALS with prevalent upper (pUMN) and lower motor neuron (pLMN) impairment. tVSA showed higher accuracy in discriminating pUMN from pLMN patients. AMR and SMR were significantly lower in patients with bulbar onset than those with spinal onset, both with and without bulbar symptoms. Furthermore, these values were also lower in patients with spinal onset associated with bulbar symptoms than in those with spinal onset alone. Additionally, AMR and SMR values correlated with the degree of dysphagia. Acoustic voice analysis may be considered a useful prognostic tool to differentiate spastic and flaccid dysarthria and to assess the degree of bulbar involvement in ALS.

PROSA-a multicenter prospective observational study to develop low-burden digital speech biomarkers in ALS and FTD

Objective: There is a need for novel biomarkers that can indicate disease state, project disease progression, or assess response to treatment for amyotrophic lateral sclerosis (ALS) and associated neurodegenerative diseases such as frontotemporal dementia (FTD). Digital biomarkers are especially promising as they can be collected non-invasively and at low burden for patients. Speech biomarkers have the potential to objectively measure cognitive, motor as well as respiratory symptoms at low-cost and in a remote fashion using widely available technology such as telephone calls. Methods: The PROSA study aims to develop and evaluate low-burden frequent prognostic digital speech biomarkers. The main goal is to create a single, easy-to-perform battery that serves as a valid and reliable proxy for cognitive, respiratory, and motor domains in ALS and FTD. The study will be a multicenter 12-months observational study aiming to include 75 ALS and 75 FTD patients as well as 50 healthy controls and build on three established longitudinal cohorts: DANCER, DESCRIBE-ALS and DESCRIBE-FTD. In addition to the extensive clinical phenotyping in DESCRIBE, PROSA collects a comprehensive speech protocol in fully remote and automated fashion over the telephone at four time points. This longitudinal speech data, together with gold standard measures, will allow advanced speech analysis using artificial intelligence for the development of speech-based phenotypes of ALS and FTD patients measuring cognitive, motor and respiratory symptoms. Conclusion: Speech-based phenotypes can be used to develop diagnostic and prognostic models predicting clinical change. Results are expected to have implications for future clinical trial stratification as well as supporting innovative trial designs in ALS and FTD.

The Role of Clinical Assessment in the Era of Biomarkers

Hippocratic Medicine revolved around the three main principles of patient, disease, and physician and promoted the systematic observation of patients, rational reasoning, and interpretation of collected information. Although these remain the cardinal features of clinical assessment today, Medicine has evolved from a more physician-centered to a more patient-centered approach. Clinical assessment allows physicians to encounter, observe, evaluate, and connect with patients. This establishes the patient-physician relationship and facilitates a better understanding of the patient-disease relationship, as the ultimate goal is to diagnose, prognosticate, and treat. Biomarkers are at the core of the more disease-centered approach that is currently revolutionizing Medicine as they provide insight into the underlying disease pathomechanisms and biological changes. Genetic, biochemical, radiographic, and clinical biomarkers are currently used. Here, we define a seven-level theoretical construct for the utility of biomarkers in neurodegenerative diseases. Level 1-3 biomarkers are considered supportive of clinical assessment, capable of detecting susceptibility or risk factors, non-specific neurodegeneration or dysfunction, and/or changes at the individual level which help increase clinical diagnostic accuracy and confidence. Level 4-7 biomarkers have the potential to surpass the utility of clinical assessment through detection of early disease stages and prediction of underlying pathology. In neurodegenerative diseases, biomarkers can potentiate, but cannot substitute, clinical assessment. In this current era, aside from adding to the discovery, evaluation/validation, and implementation of more biomarkers, clinical assessment remains crucial to maintaining the personal, humanistic, and sociocultural aspects of patient care. We would argue that clinical assessment is a custom that should never go obsolete.

Acoustic Measures of Dysphonia in Amyotrophic Lateral Sclerosis

PURPOSE

Identifying efficacious measures to characterize dysphonia in complex neurodegenerative diseases is key to optimal assessment and intervention. This study evaluates the validity and sensitivity of acoustic features of phonatory disruption in amyotrophic lateral sclerosis (ALS).

METHOD

Forty-nine individuals with ALS (40-79 years old) were audio-recorded while producing a sustained vowel and continuous speech. Perturbation/noise-based (jitter, shimmer, and harmonics-to-noise ratio) and cepstral/spectral (cepstral peak prominence, low-high spectral ratio, and related features) acoustic measures were extracted. The criterion validity of each measure was assessed using correlations with perceptual voice ratings provided by three speech-language pathologists. Diagnostic accuracy of the acoustic features was evaluated using area-under-the-curve analysis.

RESULTS

Perturbation/noise-based and cepstral/spectral features extracted from /a/ were significantly correlated with listener ratings of roughness, breathiness, strain, and overall dysphonia. Fewer and smaller correlations between cepstral/spectral measures and perceptual ratings were observed for the continuous speech task, although post hoc analyses revealed stronger correlations in speakers with less perceptually impaired speech. Area-under-the-curve analyses revealed that multiple acoustic features, particularly from the sustained vowel task, adequately differentiated between individuals with ALS with and without perceptually dysphonic voices.

CONCLUSIONS

Our findings support using both perturbation/noise-based and cepstral/spectral measures of sustained /a/ to assess phonatory quality in ALS. Results from the continuous speech task suggest that multisubsystem involvement impacts cepstral/spectral analyses in complex motor speech disorders such as ALS. Further investigation of the validity and sensitivity of cepstral/spectral measures during continuous speech in ALS is warranted.

Validation of automated pipeline for the assessment of a motor speech disorder in amyotrophic lateral sclerosis (ALS)

Background and objective

Amyotrophic lateral sclerosis (ALS) frequently causes speech impairments, which can be valuable early indicators of decline. Automated acoustic assessment of speech in ALS is attractive, and there is a pressing need to validate such tools in line with best practices, including analytical and clinical validation. We hypothesized that data analysis using a novel speech assessment pipeline would correspond strongly to analyses performed using lab-standard practices and that acoustic features from the novel pipeline would correspond to clinical outcomes of interest in ALS.

Methods

We analyzed data from three standard speech assessment tasks (i.e., vowel phonation, passage reading, and diadochokinesis) in 122 ALS patients. Data were analyzed automatically using a pipeline developed by Winterlight Labs, which yielded 53 acoustic features. First, for analytical validation, data were analyzed using a lab-standard analysis pipeline for comparison. This was followed by univariate analysis (Spearman correlations between individual features in Winterlight and in-lab datasets) and multivariate analysis (sparse canonical correlation analysis (SCCA)). Subsequently, clinical validation was performed. This included univariate analysis (Spearman correlation between automated acoustic features and clinical measures) and multivariate analysis (interpretable autoencoder-based dimensionality reduction).

Results

Analytical validity was demonstrated by substantial univariate correlations (Spearman's ρ > 0.70) between corresponding pairs of features from automated and lab-based datasets, as well as interpretable SCCA feature groups. Clinical validity was supported by strong univariate correlations between automated features and clinical measures (Spearman's ρ > 0.70), as well as associations between multivariate outputs and clinical measures.

Conclusion

This novel, automated speech assessment feature set demonstrates substantial promise as a valid tool for analyzing impaired speech in ALS patients and for the further development of these technologies.

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Introduction

In this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia.

Methods

In this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed.

Results

We found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%.

Discussion

The predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.

A Tutorial Review on Clinical Acoustic Markers in Speech Science

PURPOSE

The human voice changes with the progression of neurological disease and the onset of diseases that affect articulators, often decreasing the effectiveness of communication. These changes can be objectively measured using signal processing techniques that extract acoustic features. When measuring acoustic features, there are often several steps and assumptions that might be known to experts in acoustics and phonetics, but are less transparent for other disciplines (e.g., clinical medicine, speech pathology, engineering, and data science). This tutorial describes these signal processing techniques, explicitly outlines the underlying steps for accurate measurement, and discusses the implications of clinical acoustic markers.

CONCLUSIONS

We establish a vocabulary using straightforward terms, provide visualizations to achieve common ground, and guide understanding for those outside the domains of acoustics and auditory signal processing. Where possible, we highlight the best practices for measuring clinical acoustic markers and suggest resources for obtaining and further understanding these measures.

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

A Road Map for Remote Digital Health Technology for Motor Neuron Disease

Despite recent and potent technological advances, the real-world implementation of remote digital health technology in the care and monitoring of patients with motor neuron disease has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of patients’ ability to visit the clinic. To facilitate the wide-scale adoption of digital health care technology and to align current initiatives, we outline a road map that will identify clinically relevant digital parameters; mediate the development of benefit-to-burden criteria for innovative technology; and direct the validation, harmonization, and adoption of digital health care technology in real-world settings. We define two key end products of the road map: (1) a set of reliable digital parameters to capture data collected under free-living conditions that reflect patient-centric measures and facilitate clinical decision making and (2) an integrated, open-source system that provides personalized feedback to patients, health care providers, clinical researchers, and caregivers and is linked to a flexible and adaptable platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of motor neuron disease, the adoption of digital health care technology will significantly benefit the delivery of care and accelerate the development of effective treatments.

The relationship between pulmonary and swallowing functions in patients with neuromuscular diseases followed up by a tertiary referral center: a cross-sectional study

INTRODUCTION

Respiratory muscle weakness is common in patients with neuromuscular diseases (NMD). This puts them at risk for dysphagia and other pulmonary complications.

OBJECTIVES

To investigate the relationship between pulmonary function and swallowing in NMD.

MATERIALS AND METHODS

In this cross-sectional study, medical records of patients undergoing treatment at the Tertiary Referral Center for Neuromuscular Diseases of Hospital de Apoio de Brasília, Brazil, were reviewed. Respiratory function was assessed through spirometry (FVC and FEV1 measured) and swallowing assessed by the Dysphagia Risk Evaluation Protocol and the Functional Oral Intake Scale.

RESULTS

Two hundred and twenty-two patients were included. Dysphagia was present in 46.8% of patients and impairment of pulmonary function in 64.0%. The mean FVC observed was 66.9% and FEV1 was 66.0%, indicating restrictive lung disease. A correlation between the decline of pulmonary and swallowing functions was observed in patients with NMDs (FVC vs. DREP, R = 0.46; FVC vs. FOIS, R = 0.42; FEV1 vs. DREP, R = 0.42; FEV1 vs. FOIS, R = 0.40, p<.01). FVC and FEV1 values tend to be lower in patients with dysphagia in the context of NMD.

CONCLUSIONS

A positive correlation between pulmonary function and swallowing outcomes was observed in patients with NMD. Despite respiratory and swallowing impairment being widely present in the population with NMD, they require different treatments according to the disease's pathophysiology. Future studies should be conducted to explore the disease-specific relationship between pulmonary function and swallowing in patients with NMD.

Repeatability of Commonly Used Speech and Language Features for Clinical Applications

Introduction

Changes in speech have the potential to provide important information on the diagnosis and progression of various neurological diseases. Many researchers have relied on open-source speech features to develop algorithms for measuring speech changes in clinical populations as they are convenient and easy to use. However, the repeatability of open-source features in the context of neurological diseases has not been studied.

Methods

We used a longitudinal sample of healthy controls, individuals with amyotrophic lateral sclerosis, and individuals with suspected frontotemporal dementia, and we evaluated the repeatability of acoustic and language features separately on these 3 data sets.

Results

Repeatability was evaluated using intraclass correlation (ICC) and the within-subjects coefficient of variation (WSCV). In 3 sets of tasks, the median ICC were between 0.02 and 0.55, and the median WSCV were between 29 and 79%.

Conclusion

Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.

Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis

Bulbar deterioration in amyotrophic lateral sclerosis (ALS) is a devastating characteristic that impairs patients' ability to communicate, and is linked to shorter survival. The existing clinical instruments for assessing bulbar function lack sensitivity to early changes. In this paper, using a cohort of N = 65 ALS patients who provided regular speech samples for 3-9 months, we demonstrated that it is possible to remotely detect early speech changes and track speech progression in ALS via automated algorithmic assessment of speech collected digitally.

Speech rehabilitation in dysarthria after stroke: a systematic review of the studies

INTRODUCTION

Speech difficulties such as dysarthria or aphasia are frequently seen, in addition to motor impairments, in subjects after stroke.

EVIDENCE ACQUISITION

Literature searches with the keywords: "stroke" AND "dysarthria" AND "speech therapy" OR "language therapy" were conducted in PubMed, EMBASE, Cochrane Library and Web of Science to perform the systematic review about the several strategies used to treat dysarthria in subjects after stroke. The search was performed independently by two authors (CR and VM) from December 15th 2019 to January 15th 2020, using the PICOS criteria: participants were aging adults (>18 years old) affected by stroke; intervention was based on rehabilitation speech therapy; comparator was any comparator (all logopedic and speech rehabilitation tools); outcomes included clinical assessments, diagnostic scales and acoustic analysis of voice; and study design was RCTs, case series and case report, observational studies. The research identified a total of 94 articles for the first search and 56 for the second search. Sixty selected articles were analyzed by the reviewers. Twenty-five publications met the inclusion criteria and were included in the systematic review. Thirty-three articles were excluded for the following reasons: 12 involved individuals with aphasia or other speech problems different from dysarthria, 10 examined the clinical features of dysarthria, 3 treated on the impact of dysarthria on social participation following stroke, 8 did not include cases after stroke.

EVIDENCE SYNTHESIS

A systematic review was performed to identify the main used speech rehabilitation treatments for dysarthria after stroke. We defined the several techniques to better guide the physician to delineate a speech rehabilitation protocol adopting the better strategies described in the current literature.

CONCLUSIONS

This systematic review tried to provide to the reader a complete overview of the literature of all possible different speech treatments for dysarthria after stroke. A correct protocol could permit to improve the communication and the quality of life of these subjects.

Dysarthria and stroke. The effectiveness of speech rehabilitation. A systematic review and meta-analysis of the studies

INTRODUCTION

Speech difficulties, such as dysarthria or aphasia, in addition to motor impairments are frequently seen in post-stroke patients.

EVIDENCE ACQUISITION

Literature searches with the keywords: "stroke" and "dysarthria" and "diagnosis" and "stroke" and "dysarthria" and "assessment" were conducted using PubMed, EMBASE, Cochrane Library, and Web of Science databases to perform the systematic review about the methods used to measure the severity of dysarthria in subjects post-stroke. The search was performed by two authors from 15 January to 22 February 2020. The research identified a total of 402 articles for the search using the keywords "stroke" and "dysarthria," and "diagnosis" and 84 references for the search using the keywords "stroke" and "dysarthria" and "assessment." Sixty-nine selected articles were analyzed by the reviewers. Thirty-seven publications met the inclusion criteria and were included in the systematic review. Thirty-two articles were excluded for several reasons: 1) 12 involved individuals with aphasia or other speech problems different from dysarthria; 2) 12 examined different topics from our aim; and 3) eight did not include post-stroke cases.

EVIDENCE SYNTHESIS

The systematic review identified methods for measuring the severity of post-stroke dysarthria. The meta-analysis showed the acoustic parameters affected in dysarthria secondary to stroke and the differences in these parameters after speech therapy.

CONCLUSIONS

The alternating and sequential motion rate (AMR- Pə, AMR-Tə, AMR-Kə, and SMR-PəTəKə) and maximum phonation time were significantly improved after speech rehabilitation.