Narrative analysis in individuals with Parkinson's disease following intensive voice treatment: secondary outcome variables from a randomized controlled trial.

Communication is often impaired in individuals with Parkinson's disease (PD), typically secondary to sensorimotor deficits impacting voice and speech. Language may also be diminished in PD, particularly for production and comprehension of verbs. Evidence exists that verb processing is influenced by motor system modulation suggesting that verb deficits in PD are underpinned by similarities in the neural representations of actions that span motor and semantic systems. Conversely, subtle differences in cognition in PD may explain difficulty in processing of complex syntactic forms, which increases cognitive demand and is linked to verb use. Here we investigated whether optimizing motor system support for vocal function (improving loudness) affects change in lexical semantic, syntactic, or informativeness aspects of spoken discourse. Picture description narratives were compared for 20 Control participants and 39 with PD, 19 of whom underwent Lee Silverman Voice Treatment (LSVT LOUD®). Treated PD narratives were also contrasted with those of untreated PD and Control participants at Baseline and after treatment. Controls differed significantly from the 39 PD participants for verbs per utterance, but this difference was largely driven by untreated PD participants who produced few utterances but with verbs, inflating their verbs per utterance. Given intervention, there was a significant increase in vocal loudness but no significant changes in language performance. These data do not support the hypothesis that targeting this speech motor system results in improved language production. Instead, the data provide evidence of considerable variability in measures of language production across groups, particularly in verbs per utterance.

Immediate and long-term effects of speech treatment targets and intensive dosage on Parkinson's disease dysphonia and the speech motor network: Randomized controlled trial.

This study compared acoustic and neural changes accompanying two treatments matched for intensive dosage but having two different treatment targets (voice or articulation) to dissociate the effects of treatment target and intensive dosage in speech therapies. Nineteen participants with Parkinsonian dysphonia (11 F) were randomized to three groups: intensive treatment targeting voice (voice group, n = 6), targeting articulation (articulation group, n = 7), or an untreated group (no treatment, n = 6). The severity of dysphonia was assessed by the smoothed cepstral peak prominence (CPPS) and neuronal changes were evaluated by cerebral blood flow (CBF) recorded at baseline, posttreatment, and 7-month follow-up. Only the voice treatment resulted in significant posttreatment improvement in CPPS, which was maintained at 7 months. Following voice treatment, increased activity in left premotor and bilateral auditory cortices was observed at posttreatment, and in the left motor and auditory cortices at 7-month follow-up. Articulation treatment resulted in increased activity in bilateral premotor and left insular cortices that were sustained at a 7-month follow-up. Activation in the auditory cortices and a significant correlation between the CPPS and CBF in motor and auditory cortices was observed only in the voice group. The intensive dosage resulted in long-lasting behavioral and neural effects as the no-treatment group showed a progressive decrease in activity in areas of the speech motor network out to a 7-month follow-up. These results indicate that dysphonia and the speech motor network can be differentially modified by treatment targets, while intensive dosage contributes to long-lasting effects of speech treatments.

Remote Assessment of Parkinson's Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network.

Telemonitoring of Parkinson's Disease (PD) has attracted considerable research interest because of its potential to make a lasting, positive impact on the life of patients and their carers. Purpose-built devices have been developed that record various signals which can be associated with average PD symptom severity, as quantified on standard clinical metrics such as the Unified Parkinson's Disease Rating Scale (UPDRS). Speech signals are particularly promising in this regard, because they can be easily recorded without the use of expensive, dedicated hardware. Previous studies have demonstrated replication of UPDRS to within less than 2 points of a clinical raters' assessment of symptom severity, using high-quality speech signals collected using dedicated telemonitoring hardware. Here, we investigate the potential of using the standard voice-over-GSM (2G) or UMTS (3G) cellular mobile telephone networks for PD telemonitoring, networks that, together, have greater than 5 billion subscribers worldwide. We test the robustness of this approach using a simulated noisy mobile communication network over which speech signals are transmitted, and approximately 6000 recordings from 42 PD subjects. We show that UPDRS can be estimated to within less than 3.5 points difference from the clinical raters' assessment, which is clinically useful given that the inter-rater variability for UPDRS can be as high as 4-5 UPDRS points. This provides compelling evidence that the existing voice telephone network has potential towards facilitating inexpensive, mass-scale PD symptom telemonitoring applications.

Guidelines for Speech Recording and Acoustic Analyses in Dysarthrias of Movement Disorders.

Most patients with movement disorders have speech impairments resulting from sensorimotor abnormalities that affect phonatory, articulatory, and prosodic speech subsystems. There is widespread cross-discipline use of speech recordings for diagnostic and research purposes, despite which there are no specific guidelines for a standardized method. This review aims to combine the specific clinical presentations of patients with movement disorders, existing acoustic assessment protocols, and technological advances in capturing speech to provide a basis for future research in this field and to improve the consistency of clinical assessments. We considered 3 areas: the recording environment (room, seating, background noise), the recording process (instrumentation, vocal tasks, elicitation of speech samples), and the acoustic outcome data. Four vocal tasks, namely, sustained vowel, sequential and alternating motion rates, reading passage, and monologues, are integral aspects of motor speech assessment. Fourteen acoustic vocal speech features, including their hypothesized pathomechanisms with regard to typical occurrences in hypokinetic or hyperkinetic dysarthria, are hereby recommended for quantitative exploratory analysis. Using these acoustic features and experimental speech data, we demonstrated that the hyperkinetic dysarthria group had more affected speech dimensions compared with the healthy controls than had the hypokinetic speakers. Several contrasting speech patterns between both dysarthrias were also found. This article is the first attempt to provide initial recommendations for a standardized way of recording the voice and speech of patients with hypokinetic or hyperkinetic dysarthria; thus allowing clinicians and researchers to reliably collect, acoustically analyze, and compare vocal data across different centers and patient cohorts. © 2020 International Parkinson and Movement Disorder Society.

Evidence-based treatment of voice and speech disorders in Parkinson disease.

PURPOSE OF REVIEW: Voice and speech impairments are present in nearly 90% of people with Parkinson disease and negatively impact communication and quality of life. This review addresses the efficacy of Lee Silverman Voice Treatment (LSVT) LOUD to improve vocal loudness (as measured by vocal sound pressure level vocSPL) and functional communication in people with Parkinson disease. The underlying physiologic mechanisms of Parkinson disease associated with voice and speech changes and the strength of the current treatment evidence are discussed with recommendations for best clinical practice. RECENT FINDINGS: Two randomized control trials demonstrated that participants who received LSVT LOUD were significantly better on the primary outcome variable of improved vocSPL posttreatment than alternative and no treatment groups. Treatment effects were maintained for up to 2 years. In addition, improvements have been demonstrated in associated outcome variables, including speech rate, monotone, voice quality, speech intelligibility, vocal fold adduction, swallowing, facial expression and neural activation. Advances in technology-supported treatment delivery are enhancing treatment accessibility. SUMMARY: Data support the efficacy of LSVT LOUD to increase vocal loudness and functional communication in people with Parkinson disease. Timely intervention is essential for maximizing quality of life for people with Parkinson disease.

Robust fundamental frequency estimation in sustained vowels: detailed algorithmic comparisons and information fusion with adaptive Kalman filtering.

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required.

Effects of the Lee Silverman Voice Treatment (LSVT® LOUD) on hypomimia in Parkinson's disease.

Given associations between facial movement and voice, the potential of the Lee Silverman Voice Treatment (LSVT) to alleviate decreased facial expressivity, termed hypomimia, in Parkinson's disease (PD) was examined. Fifty-six participants--16 PD participants who underwent LSVT, 12 PD participants who underwent articulation treatment (ARTIC), 17 untreated PD participants, and 11 controls without PD--produced monologues about happy emotional experiences at pre- and post-treatment timepoints ("T1" and "T2," respectively), 1 month apart. The groups of LSVT, ARTIC, and untreated PD participants were matched on demographic and health status variables. The frequency and variability of facial expressions (Frequency and Variability) observable on 1-min monologue videorecordings were measured using the Facial Action Coding System (FACS). At T1, the Frequency and Variability of participants with PD were significantly lower than those of controls. Frequency and Variability increases of LSVT participants from T1 to T2 were significantly greater than those of ARTIC or untreated participants. Whereas the Frequency and Variability of ARTIC participants at T2 were significantly lower than those of controls, LSVT participants did not significantly differ from controls on these variables at T2. The implications of these findings, which suggest that LSVT reduces parkinsonian hypomimia, for PD-related psychosocial problems are considered.

Objective Automatic Assessment of Rehabilitative Speech Treatment in Parkinson's Disease.

Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as "acceptable" (a clinician would allow persisting during in-person rehabilitation treatment) or "unacceptable" (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as "acceptable" or "unacceptable." We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool.

Innovative technology for the assisted delivery of intensive voice treatment (LSVT®LOUD) for Parkinson disease.

PURPOSE: To assess the feasibility and effectiveness of a newly developed assistive technology system, Lee Silverman Voice Treatment Companion (LSVT(®) Companion™, hereafter referred to as "Companion"), to support the delivery of LSVT(®)LOUD, an efficacious speech intervention for individuals with Parkinson disease (PD). METHOD: Sixteen individuals with PD were randomized to an immediate (n = 8) or a delayed (n = 8) treatment group. They participated in 9 LSVT LOUD sessions and 7 Companion sessions, independently administered at home. Acoustic, listener perception, and voice and speech rating data were obtained immediately before (pre), immediately after (post), and at 6 months post treatment (follow-up). System usability ratings were collected immediately post treatment. Changes in vocal sound pressure level were compared to data from a historical treatment group of individuals with PD treated with standard, in-person LSVT LOUD. RESULTS: All 16 participants were able to independently use the Companion. These individuals had therapeutic gains in sound pressure level, pre to post and pre to follow-up, similar to those of the historical treatment group. CONCLUSIONS: This study supports the use of the Companion as an aid in treatment of hypokinetic dysarthria in individuals with PD. Advantages and disadvantages of the Companion, as well as limitations of the present study and directions for future studies, are discussed.

Intensive treatment of dysarthria secondary to stroke.

This study investigated the impact of a well-defined behavioral dysarthria treatment on acoustic and perceptual measures of speech in four adults with dysarthria secondary to stroke. A single-subject A-B-A experimental design was used to measure the effects of the Lee Silverman Voice Treatment (LSVT(®) LOUD) on the speech of individual participants. Dependent measures included vocal sound pressure level, phonatory stability, vowel space area, and listener ratings of speech, voice and intelligibility. Statistically significant improvements (p < 0.05) in vocal dB SPL and phonatory stability as well as larger vowel space area were present for all participants. Listener ratings suggested improved voice quality and more natural speech post-treatment. Speech intelligibility scores improved for one of four participants. These data suggest that people with dysarthria secondary to stroke can respond positively to intensive speech treatments such as LSVT. Further studies are needed to investigate speech treatments specific to stroke.

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease.

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

Intensive voice treatment in Parkinson's disease: Lee Silverman Voice Treatment.

Advances in neuroscience have led to an expanded and improved understanding of neurobiological changes associated with rehabilitation and exercise in Parkinson's disease (PD). This knowledge has led to a direct clinical impact of increased referral for early and continuous exercise programs for individuals with PD (physical, occupational, speech therapy and general exercise programs) and an increased research focus on the impact of such approaches in humans with PD. The purpose of this article is to examine the role of speech therapy in the landscape of exercise-based interventions for individuals with PD. We will specifically focus on the intensive voice treatment protocol, Lee Silverman Voice Treatment, as an example therapy. This article will briefly review the literature on the characteristics and features of speech and voice disorders in individuals with PD, and will discuss the impact of pharmacological and surgical treatment techniques on these disorders. This will be followed by a focus on behavioral speech treatment, specifically Lee Silverman Voice Treatment, including development of the treatment approach, documenting efficacy, discovery of unexpected outcomes and insights into the mechanism of speech disorders in PD gained from treatment-related changes. This research will be placed in the context of other previous and current speech treatment approaches in development for individuals with PD, and will highlight future directions for research.

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity.

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p<0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests.

Tracking Parkinson's disease (PD) symptom progression often uses the unified Parkinson's disease rating scale (UPDRS) that requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians' estimates), using only simple, self-administered, and noninvasive speech tests. We characterize speech with signal processing algorithms, extracting clinically useful features of average PD progression. Subsequently, we select the most parsimonious model with a robust feature selection algorithm, and statistically map the selected subset of features to UPDRS using linear and nonlinear regression techniques that include classical least squares and nonparametric classification and regression trees. We verify our findings on the largest database of PD speech in existence (approximately 6000 recordings from 42 PD patients, recruited to a six-month, multicenter trial). These findings support the feasibility of frequent, remote, and accurate UPDRS tracking. This technology could play a key part in telemonitoring frameworks that enable large-scale clinical trials into novel PD treatments.

Neural correlates of efficacy of voice therapy in Parkinson's disease identified by performance-correlation analysis.

LSVT LOUD (Lee Silverman Voice Treatment) is efficacious in the treatment of speech disorders in idiopathic Parkinson's disease (IPD), particularly hypophonia. Functional imaging in patients with IPD has shown abnormalities in several speech regions and changes in these areas immediately following treatment. This study serves to extend the analysis by correlating changes of regional neural activity with the main behavioral change following treatment, namely, increased vocal intensity. Ten IPD participants with hypophonia were studied before and after LSVT LOUD. Cerebral blood flow during rest and reading conditions were measured by H(2)(15)O-positron emission tomography. Z-score images were generated by contrasting reading with rest conditions for pre- and post-LSVT LOUD sessions. Neuronal activity during reading in the pre- versus post-LSVT LOUD contrast was correlated with corresponding change in vocal intensity to generate correlation images. Behaviorally, vocal intensity for speech tasks increased significantly after LSVT LOUD. The contrast and correlation analyses indicate a treatment-dependent shift to the right hemisphere with modification in the speech motor regions as well as in prefrontal and temporal areas. We interpret the modification of activity in these regions to be a top-down effect of LSVT LOUD. The absence of an effect of LSVT LOUD on the basal ganglion supports this argument. Our findings indicate that the therapeutic effect of LSVT LOUD in IPD hypophonia results from a shift in cortical activity to the right hemisphere. These findings demonstrate that the short-term changes in the speech motor and multimodal integration areas can occur in a top-down manner.

Formant centralization ratio: a proposal for a new acoustic measure of dysarthric speech.

PURPOSE: The vowel space area (VSA) has been used as an acoustic metric of dysarthric speech, but with varying degrees of success. In this study, the authors aimed to test an alternative metric to the VSA-the formant centralization ratio (FCR), which is hypothesized to more effectively differentiate dysarthric from healthy speech and register treatment effects. METHOD: Speech recordings of 38 individuals with idiopathic Parkinson's disease and dysarthria (19 of whom received 1 month of intensive speech therapy [Lee Silverman Voice Treatment; LSVT LOUD]) and 14 healthy control participants were acoustically analyzed. Vowels were extracted from short phrases. The same vowel-formant elements were used to construct the FCR, expressed as (F2u + F2a + F1i + F1u) / (F2i + F1a), the VSA, expressed as ABS([F1i x (F2a - F2u) + F1a x (F2u - F2i) + F1u x (F2i - F2a)] / 2), a logarithmically scaled version of the VSA (LnVSA), and the F2i /F2u ratio. RESULTS: Unlike the VSA and the LnVSA, the FCR and F2i/F2u ratio robustly differentiated dysarthric from healthy speech and were not gender sensitive. All metrics effectively registered treatment effects and were strongly correlated with each other. CONCLUSION: Albeit preliminary, the present findings indicate that the FCR is a sensitive, valid, and reliable acoustic metric for distinguishing dysarthric from unimpaired speech and for monitoring treatment effects, probably because of reduced sensitivity to interspeaker variability and enhanced sensitivity to vowel centralization.

Suitability of dysphonia measurements for telemonitoring of Parkinson's disease.

We present an assessment of the practical value of existing traditional and non-standard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, Pitch Period Entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected 10 highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that non-standard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected non-standard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well-suited to telemonitoring applications.

Speech treatment for Parkinson's disease.

Researchers estimate that 89% of people with Parkinson's disease (PD) have speech and voice disorders including disorders of laryngeal, respiratory and articulatory function. Despite the high incidence of speech and voice impairment, studies suggest that only 3-4% of people with PD receive speech treatment. Here, we review the literature on the characteristics and features of speech and voice disorders in people with PD and the types of treatment techniques available (medical, surgical and behavioral), with a focus on behavioral therapies. We provide a summary of the current status of the field of speech treatment in PD and recommendations for implementation of the current efficacy of treatment interventions. Directions for future research, including a speculative viewpoint on how the field will evolve in 5 years time, are discussed.

Translating principles of neural plasticity into research on speech motor control recovery and rehabilitation.

PURPOSE: To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. METHOD: A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural plasticity and discusses possible similarities and differences for application to speech motor control disorders. The possible involvement of neural plasticity in changes in speech production in normalcy, development, aging, and neurological diseases and disorders was considered. This report focuses on the appropriate use of functional and structural neuroimaging and the design of feasibility studies aimed at understanding how brain mechanisms are altered by environmental manipulations such as training and stimulation and how these changes might enhance the future development of rehabilitative methods for persons with speech motor control disorders. CONCLUSIONS: Increased collaboration with neuroscientists working in clinical research centers addressing human communication disorders might foster research in this area. It is hoped that this article will encourage future research on speech motor control disorders to address the principles of neural plasticity and their application for rehabilitation.