Acoustic Speech Analytics Are Predictive of Cerebellar Dysfunction in Multiple Sclerosis

Quantitative Speech Assessment in Ataxia-Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Markers

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.

Volumetric and diffusion MRI abnormalities associated with dysarthria in multiple sclerosis

Up to half of all people with multiple sclerosis experience communication difficulties due to dysarthria, a disorder that impacts the motor aspects of speech production. Dysarthria in multiple sclerosis is linked to cerebellar dysfunction, disease severity and lesion load, but the neuroanatomical substrates of these symptoms remain unclear. In this study, 52 participants with multiple sclerosis and 14 age- and sex-matched healthy controls underwent structural and diffusion MRI, clinical assessment of disease severity and cerebellar dysfunction and a battery of motor speech tasks. Assessments of regional brain volume and white matter integrity, and their relationships with clinical and speech measures, were undertaken. White matter tracts of interest included the interhemispheric sensorimotor tract, cerebello-thalamo-cortical tract and arcuate fasciculus, based on their roles in motor and speech behaviours. Volumetric analyses were targeted to Broca's area, Wernicke's area, the corpus callosum, thalamus and cerebellum. Our results indicated that multiple sclerosis participants scored worse on all motor speech tasks. Fixel-based diffusion MRI analyses showed significant evidence of white matter tract atrophy in each tract of interest. Correlational analyses further indicated that higher speech naturalness-a perceptual measure of dysarthria-and lower reading rate were associated with axonal damage in the interhemispheric sensorimotor tract and left arcuate fasciculus in people with multiple sclerosis. Axonal damage in all tracts of interest also correlated with clinical scales sensitive to cerebellar dysfunction. Participants with multiple sclerosis had lower volumes of the thalamus and corpus callosum compared with controls, although no brain volumetrics correlated with measures of dysarthria. These findings indicate that axonal damage, particularly when measured using diffusion metrics, underpin dysarthria in multiple sclerosis.

Uncertainty of Vowel Predictions as a Digital Biomarker for Ataxic Dysarthria

Dysarthria is a common manifestation across cerebellar ataxias leading to impairments in communication, reduced social connections, and decreased quality of life. While dysarthria symptoms may be present in other neurological conditions, ataxic dysarthria is a perceptually distinct motor speech disorder, with the most prominent characteristics being articulation and prosody abnormalities along with distorted vowels. We hypothesized that uncertainty of vowel predictions by an automatic speech recognition system can capture speech changes present in cerebellar ataxia. Speech of participants with ataxia (N=61) and healthy controls (N=25) was recorded during the "picture description" task. Additionally, participants' dysarthric speech and ataxia severity were assessed on a Brief Ataxia Rating Scale (BARS). Eight participants with ataxia had speech and BARS data at two timepoints. A neural network trained for phoneme prediction was applied to speech recordings. Average entropy of vowel tokens predictions (AVE) was computed for each participant's recording, together with mean pitch and intensity standard deviations (MPSD and MISD) in the vowel segments. AVE and MISD demonstrated associations with BARS speech score (Spearman's rho=0.45 and 0.51), and AVE demonstrated associations with BARS total (rho=0.39). In the longitudinal cohort, Wilcoxon pairwise signed rank test demonstrated an increase in BARS total and AVE, while BARS speech and acoustic measures did not significantly increase. Relationship of AVE to both BARS speech and BARS total, as well as the ability to capture disease progression even in absence of measured speech decline, indicates the potential of AVE as a digital biomarker for cerebellar ataxia.

Comparing dysphonia severity index, objective, subjective, and perceptual analysis of voice in patients with multiple sclerosis and healthy controls

BACKGROUND

Impairments in voice quality in Multiple Sclerosis (MS) have recently been investigated and different results were found. A voice-centered multidimensional assessment protocol with patient-reported outcome measures was conducted to evaluate all the aspects of the voice changes.

OBJECTIVES

The study aimed to compare the objective, subjective, and perceptual measures of voice between the people with MS and the healthy control group.

METHODS

A total of 128 participants, including 64 people with MS age, and gender-matched healthy controls were enrolled in the study. Subjective, objective, and auditory-perceptual voice assessments of the participants were performed. The auditory-perceptual evaluation was performed with GRBAS. The Dysphonia Severity index was computed for both groups. All the participants completed the Turkish version of The Voice Handicap Index-10 (VHI-10) and the Voice-Related Quality of Life (VRQoL).

RESULTS

Acoustic and aerodynamic parameters of voice were found significantly different for both males and females between the MS and control group. DSI was found significantly different for both males and females in the MS group compared to the control group (p<0.05). All components of the GRBAS scale were significantly higher in the MS group (p<0.001). Using a multivariate regression model, it was determined that age, gender, EDSS score, number of MS attacks, and disease duration did not affect the DSI. The overall VHI-10 score was higher in the MS group (median=1.0 range= 0-28) and lower in the control group (median=0 range= 0-4). The mean VRQoL was lower in the MS group (median=95 range= 62.5-100) than in controls (median=100 range= 85-100) (p<0.001).

CONCLUSION

Our results indicated that people with MS have significant differences in acoustic and aerodynamic parameters of voice compared to healthy individuals. A significant number of persons with MS are aware that their voice problem affects their quality of life. People with MS must be monitored for voice changes and a multidimensional voice assessment protocol should be implemented.

A Cross-sectional Study of Perceptual and Acoustic Voice Characteristics in Healthy Aging

PURPOSE

The human voice qualitatively changes across the lifespan. Although some of these vocal changes may be pathologic, other changes likely reflect natural physiological aging. Normative data for voice characteristics in healthy aging is limited and disparate studies have used a range of different acoustic features, some of which are implicated in pathologic voice changes. We examined the perceptual and acoustic features that predict healthy aging.

METHOD

Participants (N = 150) aged between 50 and 92 years performed a sustained vowel task. Acoustic features were measured using the Multi-Dimensional Voice Program and the Analysis of Dysphonia in Speech and Voice. We used forward and backward variable elimination techniques based on the Bayesian information criterion and linear regression to assess which of these acoustic features predict age and perceptual features. Hearing thresholds were determined using pure-tone audiometry tests at frequencies 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz. We further explored potential relationships between these acoustic features and clinical assessments of voice quality using the Consensus Auditory-Perceptual Evaluation of Voice.

RESULTS

Chronological age was significantly predicted by greater voice turbulence, variability of cepstral fundamental frequency, low relative to high spectral energy, and cepstral intensity. When controlling for hearing loss, age was significantly predicted by amplitude perturbations and cepstral intensity. Clinical assessments of voice indicated perceptual characteristics of speech were predicted by different acoustic features. For example, breathiness was predicted by the soft phonation index, mean cepstral peak prominence, mean low-high spectral ratio, and mean cepstral intensity.

CONCLUSIONS

Findings suggest that acoustic features that predict healthy aging are different than those previously reported for the pathologic voice. We propose a model of healthy and pathologic voice development in which voice characteristics are mediated by the inability to monitor vocal productions associated with age-related hearing loss. This normative data of healthy vocal aging may assist in separating voice pathologies from healthy aging.

Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics

Neurodegenerative disease often affects speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to delineate healthy speech from speech of people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We subjected 74 spectral and temporal prosodic features from the speech recordings to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.

Extracting Phonetic Posterior-Based Features for Detecting Multiple Sclerosis From Speech

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system which, in addition to affecting motor and cognitive functions, may also lead to specific changes in the speech of patients. Speech production, comprehension, repetition and naming tasks, as well as structural and content changes in narratives, might indicate a limitation of executive functions. In this study we present a speech-based machine learning technique to distinguish speakers with relapsing-remitting subtype MS and healthy controls (HC). We exploit the fact that MS might cause a motor speech disorder similar to dysarthria, which, with our hypothesis, might affect the phonetic posterior estimates supplied by a Deep Neural Network acoustic model. From our experimental results, the proposed posterior posteriorgram-based feature extraction approach is useful for detecting MS: depending on the actual speech task, we obtained Equal Error Rate values as low as 13.3%, and AUC scores up to 0.891, indicating a competitive and more consistent classification performance compared to both the x-vector and the openSMILE 'ComParE functionals' attributes. Besides this discrimination performance, the interpretable nature of the phonetic posterior features might also make our method suitable for automatic MS screening or monitoring the progression of the disease. Furthermore, by examining which specific phonetic groups are the most useful for this feature extraction process, the potential utility of the proposed phonetic features could also be utilized in the speech therapy of MS patients.

Speech deficits in multiple sclerosis: a narrative review of the existing literature

Multiple sclerosis (MS) is a chronic inflammatory and demyelinating autoimmune disease. MS patients deal with motor and sensory impairments, visual disabilities, cognitive disorders, and speech and language deficits. The study aimed to record, enhance, update, and delve into our present comprehension of speech deficits observed in patients with MS and the methodology (assessment tools) studies followed. The method used was a search of the literature through the databases for May 2015 until June 2022. The reviewed studies offer insight into speech impairments most exhibited by MS patients. Patients with MS face numerous communication changes concerning the phonation system (changes observed concerning speech rate, long pause duration) and lower volume. Moreover, the articulation system was affected by the lack of muscle synchronization and inaccurate pronunciations, mainly of vowels. Finally, there are changes regarding prosody (MS patients exhibited monotonous speech). Findings indicated that MS patients experience communication changes across various domains. Based on the reviewed studies, we concluded that the speech system of MS patients is impaired to some extent, and the patients face many changes that impact their conversational ability and the production of slower and inaccurate speech. These changes can affect MS patients' quality of life.

Articulatory speech measures can be related to the severity of multiple sclerosis

Background

Dysarthria is one of the most frequent communication disorders in patients with Multiple Sclerosis (MS), with an estimated prevalence of around 50%. However, it is unclear if there is a relationship between dysarthria and the severity or duration of the disease.

Objective

Describe the speech pattern in MS, correlate with clinical data, and compare with controls.

Methods

A group of MS patients (n = 73) matched to healthy controls (n = 37) by sex and age. Individuals with neurological and/or systemic conditions that could interfere with speech were excluded. MS group clinical data were obtained through the analysis of medical records. The speech assessment consisted of auditory-perceptual and speech acoustic analysis, from recording the following speech tasks: phonation and breathing (sustained vowel/a/); prosody (sentences with different intonation patterns) and articulation (diadochokinesis; spontaneous speech; diphthong/iu/repeatedly).

Results

In MS, 72.6% of the individuals presented mild dysarthria, with alterations in speech subsystems: phonation, breathing, resonance, and articulation. In the acoustic analysis, individuals with MS were significantly worse than the control group (CG) in the variables: standard deviation of the fundamental frequency (p = 0.001) and maximum phonation time (p = 0.041). In diadochokinesis, individuals with MS had a lower number of syllables, duration, and phonation time, but larger pauses per seconds, and in spontaneous speech, a high number of pauses were evidenced as compared to CG. Correlations were found between phonation time in spontaneous speech and the Expanded Disability Status Scale (EDSS) (r = - 0.238, p = 0.043) and phonation ratio in spontaneous speech and EDSS (r = -0.265, p = 0.023), which indicates a correlation between the number of pauses during spontaneous speech and the severity of the disease.

Conclusion

The speech profile in MS patients was mild dysarthria, with a decline in the phonatory, respiratory, resonant, and articulatory subsystems of speech, respectively, in order of prevalence. The increased number of pauses during speech and lower rates of phonation ratio can reflect the severity of MS.

Word finding, prosody and social cognition in multiple sclerosis

BACKGROUND

Impairments in speech and social cognition have been reported in people with multiple sclerosis (pwMS), although their relationships with neuropsychological outcomes and their clinical utility in MS are unclear.

OBJECTIVES

To evaluate word finding, prosody and social cognition in pwMS relative to healthy controls (HC).

METHODS

We recruited people with relapsing MS (RMS, n = 21), progressive MS (PMS, n = 24) and HC (n = 25) from an outpatient MS clinic. Participants completed a battery of word-finding, social cognitive, neuropsychological and clinical assessments and performed a speech task for prosodic analysis.

RESULTS

Of 45 pwMS, mean (SD) age was 49.4 (9.4) years, and median (range) Expanded Disability Severity Scale score was 3.5 (1.0-6.5). Compared with HC, pwMS were older and had slower information processing speed (measured with the Symbol Digit Modalities Test, SDMT) and higher depression scores. Most speech and social cognitive measures were associated with information processing speed but not with depression. Unlike speech, social cognition consistently correlated with intelligence and memory. Visual naming test mean response time (VNT-MRT) demonstrated worse outcomes in MS versus HC (p = .034, Nagelkerke's R²  = 65.0%), and in PMS versus RMS (p = .009, Nagelkerke's R²  = 50.2%). Rapid automatised object naming demonstrated worse outcomes in MS versus HC (p = .014, Nagelkerke's R²  = 49.1%). These word-finding measures showed larger effect sizes than that of the SDMT (MS vs. HC, p = .010, Nagelkerke's R²  = 40.6%; PMS vs. RMS, p = .023, Nagelkerke's R²  = 43.5%). Prosody and social cognition did not differ between MS and HC.

CONCLUSIONS

Word finding, prosody and social cognition in MS are associated with information processing speed and largely independent of mood. Impairment in visual object meaning perception is potentially a unique MS disease-related deficit that could be further explored and cautiously considered as an adjunct disability metric for MS.

Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study

BACKGROUND

An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria.

METHOD

The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve.

RESULTS

The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant.

CONCLUSION

Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

Multiple sclerosis (MS) is a progressive disease that often affects the cerebellum. It is characterised by demyelination, inflammation, and neurodegeneration within the central nervous system. Damage to the cerebellum in MS is associated with increased disability and decreased quality of life. Symptoms include gait and balance problems, motor speech disorder, upper limb dysfunction, and oculomotor difficulties. Monitoring symptoms is crucial for effective management of MS. A combination of clinical, neuroimaging, and task-based measures is generally used to diagnose and monitor MS. This paper reviews the present and new tools used by clinicians and researchers to assess cerebellar impairment in people with MS (pwMS). It also describes recent advances in digital and home-based monitoring for people with MS.

Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

PURPOSE

The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis.

METHOD

Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate.

RESULTS

Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions.

DISCUSSION

A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.

Digital Biomarkers in Multiple Sclerosis

For incurable diseases, such as multiple sclerosis (MS), the prevention of progression and the preservation of quality of life play a crucial role over the entire therapy period. In MS, patients tend to become ill at a younger age and are so variable in terms of their disease course that there is no standard therapy. Therefore, it is necessary to enable a therapy that is as personalized as possible and to respond promptly to any changes, whether with noticeable symptoms or symptomless. Here, measurable parameters of biological processes can be used, which provide good information with regard to prognostic and diagnostic aspects, disease activity and response to therapy, so-called biomarkers Increasing digitalization and the availability of easy-to-use devices and technology also enable healthcare professionals to use a new class of digital biomarkers-digital health technologies-to explain, influence and/or predict health-related outcomes. The technology and devices from which these digital biomarkers stem are quite broad, and range from wearables that collect patients' activity during digitalized functional tests (e.g., the Multiple Sclerosis Performance Test, dual-tasking performance and speech) to digitalized diagnostic procedures (e.g., optical coherence tomography) and software-supported magnetic resonance imaging evaluation. These technologies offer a timesaving way to collect valuable data on a regular basis over a long period of time, not only once or twice a year during patients' routine visit at the clinic. Therefore, they lead to real-life data acquisition, closer patient monitoring and thus a patient dataset useful for precision medicine. Despite the great benefit of such increasing digitalization, for now, the path to implementing digital biomarkers is widely unknown or inconsistent. Challenges around validation, infrastructure, evidence generation, consistent data collection and analysis still persist. In this narrative review, we explore existing and future opportunities to capture clinical digital biomarkers in the care of people with MS, which may lead to a digital twin of the patient. To do this, we searched published papers for existing opportunities to capture clinical digital biomarkers for different functional systems in the context of MS, and also gathered perspectives on digital biomarkers under development or already existing as a research approach.

Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

BACKGROUND AND PURPOSE

Objective measurement of speech has shown promising results to monitor disease state in multiple sclerosis. In this study, we characterize the relationship between disease severity and speech metrics through perceptual (listener based) and objective acoustic analysis. We further look at deviations of acoustic metrics in people with no perceivable dysarthria.

METHODS

Correlations and regression were calculated between speech measurements and disability scores, brain volume, lesion load and quality of life. Speech measurements were further compared between three subgroups of increasing overall neurological disability: mild (as rated by the Expanded Disability Status Scale ≤2.5), moderate (≥3 and ≤5.5) and severe (≥6).

RESULTS

Clinical speech impairment occurred majorly in people with severe disability. An experimental acoustic composite score differentiated mild from moderate (P < 0.001) and moderate from severe subgroups (P = 0.003), and correlated with overall neurological disability (r = 0.6, P < 0.001), quality of life (r = 0.5, P < 0.001), white matter volume (r = 0.3, P = 0.007) and lesion load (r = 0.3, P = 0.008). Acoustic metrics also correlated with disability scores in people with no perceivable dysarthria.

CONCLUSIONS

Acoustic analysis offers a valuable insight into the development of speech impairment in multiple sclerosis. These results highlight the potential of automated analysis of speech to assist in monitoring disease progression and treatment response.