Articulatory function in hypokinetic dysarthria: An electropalatographic examination of two cases

The present study employed electropalatography (EPG) and a nonspeech measure of lingual function to examine, in detail, the articulatory production deficits of two individuals with Parkinson disease (PD) and hypokinetic dysarthria. Participants read 10 repetitions of CV words contained within the carrier phrase I saw a _ today while wearing an EPG artificial palate. Target consonants included the alveolar stop /t/, lateral approximant /l/, and the alveolar fricative /s/ in the /a/ vowel environment. The results of the two participants were compared to an age-matched control group. Examination of the perceptual features of articulatory production, lingual strength, fine force control and endurance, tongue-palate contact patterns, and segment durations were conducted. Results of the study revealed quite different articulatory deficits in the two participants. Specifically, the articulation of Participant One (P1) was characterized by a fast rate of speech, undershooting of articulatory targets, and reduced duration of consonant closures. In contrast, Participant Two (P2) demonstrated tongue-palate contact patterns indicative of impaired lingual control in the presence of both normal and increased articulatory segment durations. Potential reasons for the differing articulatory deficits were hypothesized. The current study demonstrated that assessment with EPG identified potential causes of consonant imprecision in two individuals with hypokinetic dysarthria. Directions for speech pathology intervention, salient from the results of the study, were also noted.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

Dysarthria and quality of life in neurologically healthy elderly and patients with Parkinson's disease

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Hypokinetic disease, diseases produced by lack of exercise,

Includes bibliography.

Hypokinetic disease; diseases produced by lack of exercise,

Includes bibliography.

Articulatory imprecision in dysarthria following childhood cerebellar tumor: A perceptual and acoustic investigation of three male participants

Articulatory imprecision has been documented as a key perceptual feature of the dysarthria associated with childhood cerebellar tumor (CCT). As yet the underlying acoustic and physiological characteristics of motor speech production that contribute to this perceptual feature have not been identified. The aim of the current study was to describe perceptual and acoustic characteristics of consonant production in three children with dysarthria associated with CCT The results indicated that in all three cases the timing of articulatory movements during stop consonant production differed from that measured in a control group of same-age peers. The impact of cerebellar lesions in adulthood on articulatory gestures is used as a reference for discussing the findings of the current study with similarities evident. Also discussed are future research directions for examining the underlying acoustic or physiological basis for articulatory imprecision in children with dysarthria associated with CCT.

Borrowing from models of motor control to translate cognitive processes: Evidence for hypokinetic-hyperkinetic linguistic homologues?

In relation to motor control, the basal ganglia have been implicated in both the scaling and focusing of movement. Hypokinetic and hyperkinetic movement disorders manifest as a consequence of overshooting and undershooting GPi (globus pallidus internus) activity thresholds, respectively. Recently, models of motor control have been borrowed to translate cognitive processes relating to the overshooting and undershooting of GPi activity, including attention and executive function. Linguistic correlates, however, are yet to be extrapolated in sufficient detail. The aims of the present investigation were to: (1) characterise cognitive-linguistic processes within hypokinetic and hyperkinetic neural systems, as defined by motor disturbances; (2) investigate the impact of surgically-induced GPi lesions upon language abilities. Two Parkinsonian cases with opposing motor symptoms (akinetic versus dystonic/dyskinetic) served as experimental subjects in this research. Assessments were conducted both prior to as well as 3 and 12 months following bilateral posteroventral pallidotomy (PVP). Reliable changes in performance (i.e. both improvements and decrements) were typically restricted to tasks demanding complex linguistic operations across subjects. Hyperkinetic motor symptoms were associated with an initial overall improvement in complex language function as a consequence of bilateral PVP, which diminished over time, suggesting a decrescendo effect relative to surgical beneficence. In contrast, hypokinetic symptoms were associated with a more stable longitudinal linguistic profile, albeit defined by higher proportions of reliable decline versus improvement in postoperative assessment scores. The above findings endorsed the integration of the GPi within cognitive mechanisms involved in the arbitration of complex language functions. In relation to models of motor control, 'focusing' was postulated to represent the neural processes underpinning lexical-semantic manipulation, and 'scaling' the potential allocation of cognitive resources during the mediation of high-level linguistic tasks. (c) 2005 Elsevier Ltd. All rights reserved.

Analysis of articulatory dynamics in dysarthria following brain injury in childhood using electromagnetic articulography and electropalatography

Two physiological assessments, electromagnetic articulography (EMA) and electropalatography (EPG), were used simultaneously to investigate the articulatory dynamics in an 18-year-old male with dysarthria 9 years following traumatic brain injury (TBI). Eight words consisting of /t, s, integral, k/ in word initial and word final positions were produced up to 10 times. A nonneurologically impaired male served as a control subject. Six parameters were analyzed using EMA: velocity, acceleration, deceleration, distance, duration, and motion path of tongue movements. Using EPG, the pattern and amount of tongue-to-palate contact and the duration of the closure/constriction phase of each consonant produced were assessed. Timing disturbances in the TBI speaker's speech were highlighted in perceptual assessments in the form of prolonged phonemes and a reduced speech rate. EMA analysis revealed that the approach and release phase durations of the consonant productions were within normal limits. Kinematic strategies such as decreased velocity and decreased distances traveled by the tongue, however, may have counterbalanced each other to produce these appropriate results. EPG examination revealed significantly longer closure/constriction phase periods, which may have contributed to the prolonged phonemes and reduced speech rate observed. The implications of these findings for the development of treatment programs for dysarthria subsequent to TBI will be highlighted.

Treating dysarthria following traumatic brain injury: Investigating the benefits of commencing treatment during post-traumatic amnesia in two participants

Primary objective: The aims of this preliminary study were to explore the suitability for and benefits of commencing dysarthria treatment for people with traumatic brain injury (TBI) while in post-traumatic amnesia ( PTA). It was hypothesized that behaviours in PTA don't preclude participation and dysarthria characteristics would improve post-treatment. Research design: A series of comprehensive case analyses. Methods and procedures: Two participants with severe TBI received dysarthria treatment focused on motor speech deficits until emergence from PTA. A checklist of neurobehavioural sequelae of TBI was rated during therapy and perceptual and motor speech assessments were administered before and after therapy. Main outcomes and results: Results revealed that certain behaviours affected the quality of therapy but didn't preclude the provision of therapy. Treatment resulted in physiological improvements in some speech sub-systems for both participants, with varying functional speech outcomes. Conclusions: These findings suggest that dysarthria treatment can begin and provide short-term benefits to speech production during the late stages of PTA post-TBI.