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Parkinson Disease PD is a neuromotor illness affecting general movements of different muscles, those implied in speech production being among them. The relevance of speech in monitoring illness progression has been documented in dl last two decades. Most of the studies have concentrated in dysarthria and dysphonia induced by the syndrome. The present work is devoted to explore how PD affects the dynamic behavior of the speech neuromotor biomechanics neuromechanics involved in deficient articulation dysarthriain contrast to classical measurements based on static features as extreme and central vowel triangle positions.
A statistical distribution of the kinematic velocity of controol lower jaw and tongue is introduced, which presents interesting properties regarding pattern recognition and classification.
This function may be used to establish distances between different articulation profiles in terms of information theory. Results show that these distances are correlated with a set of tests currently used by neurologists in PD progress evaluation, and could be used in elaborating new speech testing protocols.
Monitoring Parkinson Disease from speech articulation kinematics. Loquens, 4 1e The neuromotor disorder known as Parkinson Disease is a sickness produced by a deficit of the neurotransmitter dopamine in basal ganglia, resulting in hampered neuromotor activity during an early phase, and leading to a movement impairing disease accompanied by cognitive disability at later stages.
As it affects most neuromotor paths, it also interferes with speech capability in different ways, which have been extensively well documented Mekyska et al. Rough and asthenic phonation, monotonicity, mono-loudness, freezing, velo-pharyngeal incompetence, and low tone are some of the observed alterations of speech coined with the term hypokinetic dysarthria Sapir, Historically, a pioneering study on PD phonation correlates with clinical purpose using distortion features as jitter, shimmer, and noise-harmonic ratio, on sustained vowels, was due to Gamboa et al.
On the one hand, to study the influence of disease progress, neurologists have also used other motor and non-motor symptoms. A possible description of the neuromotor activity from speech can be given in terms of the dynamic changes experimented by the resonant frequencies of the vocal tract, which are known classically as formants.
CONTROL NEUROMOTOR DEL HABLA by camila rivera on Prezi
The aim of the present study is to evaluate if features derived from the dynamic behavior of formants in sustained vowels are related with some of the indices used by neurologists, and to establish to which extent dynamic measures can be used in the multimodal study of PD speech production.
The structure of the present paper is as follows: Speech is the result of a biomechanical sequence of actuations led by neuromotor activity of chest, neck, oral, nasal and facial muscles under strict coordination.
This complex activity driven by primary neurons is transformed into neuromotor actions to excite muscle fibers through the intermediation of basal ganglia, where secondary neurons connected to the muscles of the pharynx, tongue, larynx, chest and diaphragm through sub-thalamic secondary pathways produce sequences of motor actions which activate the respiratory, phonatory and articulatory systems responsible for speech production neuromechanics.
The jaw bone is represented in light grey, the tongue structure is represented in light orange. Regarding the biomechanical model, the jaw J is fixed to the skull bone at fulcrum F as in a third-class lever system. The tongue T is supported by the lower jaw and hyoid bone as well as other facial tissues associated.
Lateral movements orthogonal to the sagittal plane are assumed small enough to be neglected two degrees of freedom. The phonation and articulation systems are governed by specific neuromotor units activated from the bulbar structures in the midbrain 1which control the retraction of the velopharyngeal switch in nasalization 2activate tongue movements up, down, back and forth 3: Through this study only the subsystems 3 and 4 are to be considered regarding speech characterization in relation to PD neurodegeneration.
Speech articulation neuromotor and biomechanical system. The way in which this disease alters normal speech articulation may be attributed to fine movement control, which requires proprioceptive feedback.
This means that sensor neurons in the activated muscles must send information back to the midbrain in order to adjust the ongoing neuromotor flow to muscles for finely tunning the muscular forces exerted on the biomechanical structures of the jaw-tongue system.
The feedback is modulated in short and long loops, depending on the role played by the cerebelum and other structures processing the flow from sensory units in the peripherical muscles. These feedback loops may not work properly when connections to neuromotor units are affected by some kind of problem, as for instance, lack of neurotransmitters. Therefore, hypo- hyper- or unstable muscle tone are markers of possible PD neurodegeneration, and these can be traced from speech. Speech articulation is also a possible target to explore.
The association between articulation gestures open—close and back—front positions summarizes in a simple way what researchers have established formally Sanguineti et al.
As vowel positions are related to formant associations, a possible way to infer articulation dynamics could be formant kinematics Carmona-Duarte et al. A functional relationship could be established between formants and the reference point as:.
This relationship is known to be one-to-many, i. It will also be assumed that the system given by 1 may be considered linear, time-invariant and invertible:. The algorithmic methodology implied in the process of deriving kinematic variables from acoustical ones depends on the estimation and association of formant derivatives in time with the reference point kinematics as:.
It may be hypothesized that the dorsal-ventral velocity will be mostly related to changes in the second formant back—frontand that the caudal-rostral velocity will be related to the dynamics of the first formant up—down.
This is equivalent to considering that w 11 and w 22 will be negligible compared to w 12 and w Therefore, the absolute kinematic velocity AKV of the reference point may be stated as:.
Reliable estimates for these scale factors may be obtained from diphthong articulations involving changes in the positions of the reference point which show a fast and steady change. Formant positions are obtained either from the local maxima of the transfer function in each time instant or from the polar positions of the prediction polynomials used to invert the speech segment.
An dek from an utterance of the five vowels [a: Spectrogram of an utterance with the five vowels [a: The first two formants were smoothened to eliminate glitches and other hazards and artifacts on the kinematic variables by a low-order predictive filter. Formant kinematics normative subject. Reference point absolute velocity. The zero degrees of habbla reference angle correspond to the horizontal movement nduromotor the reference point.
The large loops in the right part of the polar plot in Figure 6a are related to changes of the reference point due to adjustments of the jaw-tongue system in vowel onsets, whereas vowel nuclei activity is seen as a cloud of small amplitude actions near the center. Tremor and articulation instability can be clearly appreciated in this case, both in harmonics and in formants. Havla must be conttol that tremor in harmonics and formants need not be correlated as they depend on different neuromotor pathways, phonation being controlled by laryngeal nerves vaguswhereas articulation depends mainly on jaw, lingual and facial neuroomotor branches of trigeminaland PD cotrol affect both systems differently.
Formant kinematics PD patient. It may be seen that horizontal and vertical velocities behave quite differently than in the normative case. At this point, it will become clear that contol AKV probability density function may be a good candidate marker to establish differentiation between stable and unstable articulation due to PD. Taking into account that the AKV is a correlate of the horizontal and vertical movement speed of the jaw-tongue reference point, the kinematic behavior of the utterance is coded in its probability density function, evaluated from a histogram of counts, as follows:.
Simulated AKV probability density functions from a target pathologic speaker and a model normative speaker. The way in which probability functions distribute on these four regions will depend on the kind of pathology being monitored, and on the type of utterance being analyzed. For instance, in pathologies were sentences are used to detect the residual articulation competence, as in Amyotrophic Lateral Sclerosis, it will be expected that distributions from patients will show less activity in R3 and R4 than controls.
In certain cognitive pathologies where fluency is affected it will be expected a stronger number of counts in R1 relative to controls if continuous nwuromotor is neuromoror. In the case of PD patients uttering sustained vowels or sequences of vowels, the activity in Neurommotor, R3 and R4 is expected to be larger than in controls.
The AKV probability distribution from a healthy subject diamond marks is expected to follow a faster decay than the one from a PD patient when uttering sustained vowels. As the area under both distributions must equal one—being probability distributions—the control subject distribution is expected to start from a larger value than the pathological one.
The estimated KLD between both probability density functions in this example is 7. The described methodology is used to monitor a database of the sustained vowel [a: In what follows utterances from PD patients is compared with similar utterances dsl by healthy controls using KLD as the differentiation feature. Recordings of long aloud vowels were selected and processed to obtain the first two formants, and the respective distributions of their AKV were estimated as referred before.
The control set consisted in utterances of sustained [a: Probability density functions of the AKV from female healthy controls filesright axis and female PD patients files nsuromotor, right axis.
The AKV is given in cm. These model sets were clntrol to estimate the accumulated D KL of each PD patient of both genders against their respective model set, as.
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A global composite score CS has been used to represent the set of objective scores in a single value as:. Linear regression lines have been drawn and formulated for comparison purposes. The first observation from the results presented is that the male set presents better correlation between D KL and CS than the female one. This is not an uncommon situation in this kind of studies, as generally models and analysis protocols were initially designed on a male population, and, only latter on, they were adapted to a female population.
This underlying fact may be behind gender skew. Besides, the larger number of male cases available in the version of the PARCZ database currently used could also have an influence in these results. Another factor possibly influencing gender skew may be the wider spread of formant frequencies in female voice, which would introduce more dispersion in the results.
Another factor of dispersion to be taken into account is the variability and low reliability of subjective scoring scales. No matter how well designed they may be or well-trained raters are, a human subjective factor is implicit and difficult to be removed.
This fact stresses the need of developing objective scoring methods, especially in relation to speech production. But, in general, it may be concluded that a certain degree of correlation between formant dynamics and a wide set of motor and non-motor scoring scales exist in PD, and could be conveniently exploited if fused with other articulation static features such as VSA or FCR Sapir et al. The field of speech analysis for biomedical applications is of great interest, and it opens new and exciting research lines to speech scientists.
It is multidisciplinary and complex, therefore cooperation among large research groups including linguists, speech therapists, information processing experts, neurologists, psychologists, etc. Databases are to be collected based on new premises. Research on new feature estimation and machine learning paradigms are to be proposed. It is expected that in the next years to come neurodegenerative diseases will be monitored by speech as a daily routine Cecchi, Here the term neuromechanical is summarizing the dynamic model of the biological structure biomechanical in considering muscles, tissues, forces and velocities as well as the neuromotor spiking activity on the neuromotor pathways involved in the processes of speech production.
Kinematic modelling of diphthong articulation. With AI, our words will be a window into our mental health.
Monitoring Parkinson Disease from speech articulation kinematics
Elements of information theory. Renewal of the Neurophysiology of Language: Physiological Reviews, 85, Assessing correlations between lingual movements and formants.
Speech Communication,55 2 Prevalence, phenomenology and risk factors. Brain, 4 Journal of Voice, 11 3 Process, format, and clinimetric testing plan.