Cross-Corpus Disparity of Parkinson’s Voice Datasets Observed on Control Group Distribution

arkinson’s disease (PD) is one of the most common neurodegenerative disorders. PD has been the fastest growth in prevalence, and it has become the leading cause of disability. The severity or progression of PD can be reduced if diagnosed at the early stages. It is therefore necessary to develop rapid and simple screening methods or tools to diagnose PD. Speech impairment is one of the early symptoms of PD which is commonly termed Parkinsonian hypokinetic dysarthria. Many researchers have developed a computerized method to identify of diagnosing PD based on voice features. However, the inaccuracy of the developed models was inconsistent especially when being tested on different datasets. The possible cause is the unwanted variability and biases between datasets. This study investigates the possible inconsistencies between Parkinson’s voice datasets. The inconsistencies were investigated in the statistical distribution of voice parameters of the healthy-control (HC) group. This work observes the statistical distribution of sustained phoneme parameters extracted from the healthy- control (HC) group of five datasets using ANOVA and the Post- Hoc Turkey-Cramer test. The result suggests that the diversity in language and ethnicity were not contributing significantly to any biases between databases. The other result confirms that noises in the recording contribute to the biases in the extracted voice features, especially the harmonic feature

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Unveiling the impact of neuromotor disorders on speech: a structured approach combining biomechanical fundamentals and statistical machine learning

Speech has been shown to convey clinically useful information in the study of Neurodegenerative Disorders (NDs), such as Parkinson’s Disease (PD). Traditionally the use of speech as an exploratory tool in People with Parkinson’s (PwP) has focused on the estimation of acoustic characteristics and their study at face value, analysing the physio-acoustical markers and using them as features for the differentiation between Healthy Controls (HC) and PwP. The present work takes a step further, given the intricate interoperation between neuromotor activity, responsible for both planning and driving the system, and the production of the acoustic speech signal; by the study of speech, this relationship may be properly exploited and analysed, providing a non-invasive method for the diagnosis, analysis, and observation of NDs. This work aims to introduce a working model that is capable of linking both domains and serves as a projection tool to provide insights about a speaker’s neuromotor state. This is based on a review of the neurophysiological background of the structure and function of the nervous system, and a review of the main nervous system dysfunctions involved in PD and other related neuromotor disorders. The role of the respiratory, phonatory, and articulatory systems is reviewed in the production of voice and speech under normal and pathological circumstances. This setting might allow for speech to be considered a useful trait within the precision medicine framework, as it provides a personal biometric marker that is innate and easy to elicit, can be recorded remotely with inexpensive equipment, is non-invasive, cost-effective, and easy to process. The problem can be divided into two main categories: firstly, a binary detection task distinguishing between healthy controls and individuals with NDs based on the projection model and phonatory estimates; secondly, a progression and tracking task providing a set of quantitative indices that enable clinically interpretable scores. This study aims to define a set of features and models that help to characterise hypokinetic dysarthria (HD). These incorporate the neuroscientific knowhow semantically and quantitatively to be used in clinical decision support tools that provide mechanistic insight on the processes involved in speech production, incorporating into the algorithmic element neuromotor considerations that add to better interpretability, consequently leading to improved clinical decisions and diagnosis. An overview of the acoustic signal processing algorithms for use in speech articulation and phonation system inversion regarding neuromotor disorder assessment is provided. An algorithmic methodology for model inversion and exploration has been proposed for the functional characterization and system inversion of each subsystem involved under the neuro-biomechanical foundations exposed before. A description of the vocal fold biomechanics using the glottal source, and formant dynamics provides the base for specific mapping to articulation kinematics. The statistical methods used in performance evaluation are based on three-way comparisons and transversal and longitudinal assessment by classical hypothesis testing. Three related experimental studies are shown to empirically illustrate the potential of phonation and articulation analysis: the characterization of PD from glottal biomechanics based on the amplitude distributions of the glottal flow and on the vocal fold body stiffness in assessing the efficiency of transcranial magnetic stimulation, and the description of PD dysarthria through an articulation projection model. The results from the biomechanical analysis of phonation showed that the behaviour of glottal source amplitude distributions from PD and healthy controls using three-way comparisons and hierarchical clustering were essentially distinguishable from those from normative young participants with the best accuracy scores produced by SVM classifiers of 94.8% (males) and 92.2% (females). Nevertheless, PD participants were barely separable from age-matched controls, possibly pointing to confounding factors due to age. The outcomes from using vocal fold stiffness in assessing the efficiency of transcranial magnetic stimulation showed mixed results, as some PD participants reflected clear improvements in phonation stability after stimulation, whereas some others did not. Some cases of sham controls experienced also minor improvements of unknown origin, possibly expressing a placebo effect. The overall results on the efficiency of stimulation showed an accuracy global score of 67% over the 18 cases studied. The results from articulation projection modelling showed the possibility of formulating personalised models for PD and control participants to transform acoustic formant dynamics into articulation kinematics. This might open the possibility of characterising PD dysarthria based on speech audio records. The most remarkable findings of the study include the determination of the glottal source amplitude distribution behaviour of normative and PD participants; the impact of age effects in phonation as a confounding factor in neuromotor disorder characterization; the importance of ensuring that the classification of speech dysarthria is based on principles that can be explained and interpreted; the need of taking into account the effects of medication when framing new classification experiments; the potential of using EEG-band decomposition to analyse vocal fold stiffness correlates, as well as the possibility of using these descriptions in longitudinal monitoring of treatment efficiency; the feasibility of establishing a relationship between acoustic and kinematic variables by projection model inversion; and the potential of these descriptions for estimating neuromotor activities in midbrain related to phonation and articulation activity. The most important outcome to be brought forth from the thesis is that the methodology used throughout the project uses a bottom-up approach based on speech model inversion at the acoustical, biomechanical, and neuromotor levels allowing to estimate glottal signals, biomechanical correlates, and neuromotor activity from speech alone, establishing a common neuromechanical characterisation framework on its own

An investigation of sign dysarthria

This study explores the nature of sign production in individuals with neurogenic movement disorders. The research goals are to broadly define the phenomenon of dysarthria in signed language; to determine whether anything other than the set of articulators involved differentiates it from dysarthria in spoken language; and to delineate the differences between sign dysarthria and apraxia, and between sign dysarthria and disruption of simple limb movements. In the same way that hearing people may exhibit speech dysarthria in the absence of oral apraxia, deaf signers may, in some cases, exhibit sign dysarthria in the absence of higher level ideomotor impairments. Conversely, just as many movement disorders are more apparent in speech than in simple limb movements, sign dysarthria may also arise in the absence of severe impairment of simple movements, such as reaching or pointing. An ancillary question that this research addresses is the establishment of articulatory measures of sign dysarthria, and of normal signing. Findings from this study indicate that dysarthria, as distinct from apraxia, aphasia, and loss of simple movement, does manifest itself in sign language, which suggests that speech motor control research should eschew models of dysarthria framed around specific articulators, in favour of those that emphasize patterns of movement. However, just as dysarthria is not articulator-specific, it is also not fundamentally linguistic in nature. The reason that dysarthria can occur in either a vocal or a manual language modality is because both use very rapid, complex, co-ordinated movements. The movement speed and complexity facilitate the rapid information transfer that is necessary for any linguistic system, but that does not make disruptions to it inherently linguistic. One would predict that subjects with dysarthria would also be impaired at any task with similar motor demands, but since few normal activities require such a high level of movement precision, deficits manifest themselves primarily in speech or sign

Development of Markerless Systems for Automatic Analysis of Movements and Facial Expressions: Applications in Neurophysiology

This project is focused on the development of markerless methods for studying facial expressions and movements in neurology, focusing on Parkinson’s disease (PD) and disorders of consciousness (DOC). PD is a neurodegenerative illness that affects around 2% of the population over 65 years old. Impairments of voice/speech are among the main signs of PD. This set of impairments is called hypokinetic dysarthria, because of the reduced range of movements involved in speech. This reduction can be visible also in other facial muscles, leading to a hypomimia. Despite the high percentage of patients that suffer from dysarthria and hypomimia, only a few of them undergo speech therapy with the aim to improve the dynamic of articulatory/facial movements. The main reason is the lack of low cost methodologies that could be implemented at home. DOC after coma are Vegetative State (VS), characterized by the absence of self-awareness and awareness of the environment, and Minimally Conscious State (MCS), in which certain behaviors are sufficiently reproducible to be distinguished from reflex responses. The differential diagnosis between VS and MCS can be hard and prone to a high rate of misdiagnosis (~40%). This differential diagnosis is mainly based on neuro-behavioral scales. A key role to plan the rehabilitation in DOC patients is played by the first diagnosis after coma. In fact, MCS patients are more prone to a consciousness recovery than VS patients. Concerning PD the aim is the development of contactless systems that could be used to study symptoms related to speech and facial movements/expressions. The methods proposed here, based on acoustical analysis and video processing techniques could support patients during speech therapy also at home. Concerning DOC patients the project is focused on the assessment of reflex and cognitive responses to standardized stimuli. This would allow objectifying the perceptual analysis performed by clinicians

Functional Biomarkers to Assess Visual System Integrity: An eye tracking based approach:Functional Biomarkers to Assess Visual System Integrity

Functional Biomarkers to Assess Visual System Integrity: An eye tracking based approac

Functional Biomarkers to Assess Visual System Integrity: An eye tracking based approach:Functional Biomarkers to Assess Visual System Integrity

Functional Biomarkers to Assess Visual System Integrity: An eye tracking based approac