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Parkinson's disease (PD) is a neurodegenerative condition with constantly increasing prevalence rates, affecting strongly life quality in terms of neuromotor and cognitive performance. PD symptoms include voice and speech alterations, known as hypokinetic dysarthria (HD). Unstable phonation is one of the manifestations of HD. Repetitive transcranial magnetic stimulation (rTMS) is a rehabilitative treatment thathas been shown to improve some motor and non-motor symptoms of persons with PD (PwP). This study analyzed the phonation functional behavior of 18 participants (13 males, 5 females) with PD diagnosis before (one pre-stimulus) and after (four post-stimulus) evaluation sessions of rTMS treatment, to assess the extent of changes in their phonation stability. Participants were randomized 1:1 to receive either rTMS or sham stimulation. Voice recordings of a sustained vowel [a:] taken immediately before and after the treatment, and at follow-up evaluation sessions (immediately after, at six, ten, and fourteen weeks after the baseline assessment) were processed by inverse filtering to estimate a biomechanical correlate of vocal fold tension. This estimate was further band-pass filtered into EEG-related frequency bands. Log-likelihood ratios (LLRs) between pre- and post-stimulus amplitude distributions of each frequency band showed significant differences in five cases actively stimulated. Seven cases submitted to the sham protocol did not show relevant improvements in phonation instability. Conversely, four active cases did not show phonation improvements, whereas two sham cases did. The study provides early preliminary insights into the capability of phonation quality assessment by monitoring neuromechanical activity from acoustic signals in frequency bands aligned with EEG ones. [ABSTRACT FROM AUTHOR]

Pathological voice characterization has received increasing attention over the last 20 years. Hundreds of studies have been published showing inventive approaches with very promising findings. Nevertheless, methodological issues might hamper performance assessment trustworthiness. This study reviews some critical aspects regarding data collection and processing, machine learning-oriented methods, and grounding analytical approaches, with a view to embedding developed clinical decision support tools into the diagnosis decision-making process. A set of 26 relevant studies published since 2010 was selected through critical selection criteria and evaluated. The model-driven (MD) or data-driven (DD) character of the selected approaches is deeply examined considering novelty, originality, statistical robustness, trustworthiness, and clinical relevance. It has been found that before 2020 most of the works examined were more aligned with MD approaches, whereas over the last two years a balanced proportion of DD and MD-based studies was found. A total of 15 studies presented MD characters, whereas seven were mainly DD-oriented, and four shared both profiles. Fifteen studies showed exploratory or prospective advanced statistical analysis. Eighteen included some statistical validation to avail claims. Twenty-two reported original work, whereas the remaining four were systematic reviews of others' work. Clinical relevance and acceptability by voice specialists were found in 14 out of the 26 works commented on. Methodological issues such as detection and classification performance, training and generalization capability, explainability, preservation of semantic load, clinical acceptance, robustness, and development expenses have been identified as major issues in applying machine learning to clinical support systems. Other important aspects to be taken into consideration are trustworthiness, gender-balance issues, and statistical relevance. [ABSTRACT FROM AUTHOR]

Complex simultaneous neuropsychophysiological mechanisms are responsible for the processing of the information to be transmitted and for the neuromotor planning of the articulatory organs involved in speech. The nature of this set of mechanisms is closely linked to the clinical state of the subject. Thus, for example, in populations with neurodevelopmental deficits, these underlying neuropsychophysiological procedures are deficient and determine their phonation. Most of these cases with neurodevelopmental deficits are due to a genetic abnormality, as is the case in the population with Smith–Magenis syndrome (SMS). SMS is associated with neurodevelopmental deficits, intellectual disability, and a cohort of characteristic phenotypic features, including voice quality, which does not seem to be in line with the gender, age, and complexion of the diagnosed subject. The phonatory profile and speech features in this syndrome are dysphonia, high f0, excess vocal muscle stiffness, fluency alterations, numerous syllabic simplifications, phoneme omissions, and unintelligibility of speech. This exploratory study investigates whether the neuromotor deficits in children with SMS adversely affect phonation as compared to typically developing children without neuromotor deficits, which has not been previously determined. The authors compare the phonatory performance of a group of children with SMS (N = 12) with a healthy control group of children (N = 12) matched in age, gender, and grouped into two age ranges. The first group ranges from 5 to 7 years old, and the second group goes from 8 to 12 years old. Group differences were determined for two forms of acoustic analysis performed on repeated recordings of the sustained vowel /a/ F1 and F2 extraction and cepstral peak prominence (CPP). It is expected that the results will enlighten the question of the underlying neuromotor aspects of phonation in SMS population. These findings could provide evidence of the susceptibility of phonation of speech to neuromotor disturbances, regardless of their origin. [ABSTRACT FROM AUTHOR]

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson's Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation. [ABSTRACT FROM AUTHOR]

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson's Disease. [ABSTRACT FROM AUTHOR]

Neurodegenerative pathologies as Parkinson's Disease (PD) show important distortions in speech, affecting fluency, prosody, articulation and phonation. Classically, measurements based on articulation gestures altering formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been proposed to measure speech distortion, but these markers are based mainly on static positions of sustained vowels. The present study introduces a measurement based on the mutual information distance among probability density functions of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the jaw and tongue articulation gestures is estimated and modeled statistically. The distribution of this feature may differentiate PD patients from normative speakers during sustained vowel emission. The study is based on a limited database of 53 male PD patients, contrasted to a very selected and stable set of eight normative speakers. In this sense, distances based on Kullback–Leibler divergence seem to be sensitive to PD articulation instability. Correlation studies show statistically relevant relationship between information contents based on articulation instability to certain motor and nonmotor clinical scores, such as freezing of gait, or sleep disorders. Remarkably, one of the statistically relevant correlations point out to the time interval passed since the first diagnostic. These results stress the need of defining scoring scales specifically designed for speech disability estimation and monitoring methodologies in degenerative diseases of neuromotor origin. [ABSTRACT FROM AUTHOR]

Background. Paralysis of one vocal fold leads to glottal gap and vocal fold insufficiency that has significant impact upon a patient’s quality of life. Fillers have been tested to perform intracordal injections, but they do not provide perdurable results. Early data suggest that enriching fat grafts with adipose-derived regenerative cells (ADRCs) promote angiogenesis and modulate the immune response, improving graft survival. The aim of this study is to propose ADRC-enriched adipose tissue grafts as effective filler for the paralyzed vocal fold to use it for functional reconstruction of the glottal gap. Method. This is the first phase I-IIA clinical trial (phase I/IIA clinical trial, unicentric, randomized, controlled, and two parallel groups), to evaluate the safety of a new therapy with ADRC-enriched fat grafting (ADRC: group I) for laryngoplasty after unilateral vocal fold paralysis. Control group patients received centrifuged autologous fat (CAF: group II) grafts. Overall mean age is 52.49 ± 16.60 years. Group I (ADRC): 7 patients (3 males and 4 females), 52.28 ± 20.95 year. Group II (CAF): 7 patients (3 males and 4 females), 52.71 ± 12.59 year. Results. VHI-10 test showed that preoperative mean score was 24.21 ± 8.28. Postoperative mean score was 6.71 ± 6.75. Preoperative result in group I was 21.14 ± 3.58 and postoperative result was 3.14 ± 3.53. Preoperative result for group II was 27.29 ± 10.66. Postoperative score in group II was 10.29 ± 7.52. Wilcoxon and the Student t-tests showed that the patient’s self-perception of posttreatment improvement is larger when ADRCs are used. Comparing pre- and posttreatment voice quality analysis, group I showed a p=0.053. Group II showed a p=0.007. There would be no significant differentiation between pre- and posttreatment results. This is true for group II and limited for group I. Conclusions. This prospective trial demonstrates the safety and efficacy of the treatment of glottal gap defects utilizing ADRC-enriched fat grafts. This trial is registered with NCT02904824. [ABSTRACT FROM AUTHOR]

Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech. Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set. Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females). Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease. [ABSTRACT FROM AUTHOR]

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Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond trying to relate the biometrical signature of voice with a possible neural activity that might generate alterations in voice production. A total of 68, acoustical, glottal and biomechanical parameters were extracted from neutral and stressed speeches. The importance of the parameters was evaluated using t-test, entropy, Receiver Operator Characteristic (ROC) and Wilcoxon methods and support vector machines algorithms for classification. The emotion under study is the stress produced when a speaker has to defend an idea opposite to his/her thoughts or feelings, and this stress is compared to self-consistent speech. The results show tremor in the vocal folds to be the most relevant feature. [ABSTRACT FROM AUTHOR]

The dramatic impact of neurological degenerative pathologies in life quality is a growing concern nowadays. Many techniques have been designed for the detection, diagnosis, and monitoring of the neurological disease. Most of them are too expensive or complex for being used by primary attention medical services. On the other hand, it is well known that many neurological diseases leave a signature in voice and speech. Through the present paper, a new method to trace some neurological diseases at the level of phonation will be shown. In this way, the detection and grading of the neurological disease could be based on a simple voice test. This methodology is benefiting from the advances achieved during the last years in detecting and grading organic pathologies in phonation. The paper hypothesizes that some of the underlying neurological mechanisms affecting phonation produce observable correlates in vocal fold biomechanics and that these correlates behave differentially in neurological diseases than in organic pathologies. A general description about the main hypotheses involved and their validation by acoustic voice analysis based on biomechanical correlates of the neurological disease is given. The validation is carried out on a balanced database of normal and organic dysphonic patients of both genders. Selected study cases will be presented to illustrate the possibilities offered by this methodology. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue which include one on the methods applied in the voice quality analysis, one on the system that discriminates the normal and pathological voices, and one on the importance of voice quality assessment.

Voice diseases have been increasing dramatically in recent times due mainly to unhealthy social habits and voice abuse. These diseases must be diagnosed and treated at an early stage, especially in the case of larynx cancer. It is widely recognized that vocal and voice diseases do not necessarily cause changes in voice quality as perceived by a listener. Acoustic analysis could be a useful tool to diagnose this type of disease. Preliminary research has shown that the detection of voice alterations can be carried out by means of Gaussian mixture models and short-term mel cepstral parameters complemented by frame energy together with first and second derivatives. This paper, using the F-Ratio and Fisher's discriminant ratio, will demonstrate that the detection of voice impairments can be performed using both mel cesptral vectors and their first derivative, ignoring the second derivative. [ABSTRACT FROM AUTHOR]