This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
Although voice therapy is the first line treatment for muscle-tension voice disorders (MTVD), no clinical research has investigated the role of specific active ingredients. This study aimed to evaluate the efficacy of active ingredients in the treatment of MTVD. A retrospective review of a clinical voice database was conducted on 68 MTVD patients who were treated using the optimal phonation task (OPT) and sob voice quality (SVQ), as well as two different processes: task variation and negative practice (NP). Mixed-model analysis was performed on auditory-perceptual and acoustic data from voice recordings at baseline and after each technique. Active ingredients were evaluated using effect sizes. Significant overall treatment effects were observed for the treatment program. Effect sizes ranged from 0.34 (post-NP) to 0.387 (post-SVQ) for overall severity ratings. Effect sizes ranged from 0.237 (post-SVQ) to 0.445 (post-NP) for a smoothed cepstral peak prominence measure. The treatment effects did not depend upon the MTVD type (primary or secondary), treating clinicians, nor the number of sessions and days between sessions. Implementation of individual techniques that promote improved voice quality and processes that support learning resulted in improved habitual voice quality. Both voice techniques and processes can be considered as active ingredients in voice therapy.
The subject of vocal changes accompanying pathological conditions, although still not well explored, seems to be promising. The discovery of laryngeal receptors for sex hormones and thyroid hormones can strongly support the hypothesis of changes in voice due to various endocrinopathies. On the other hand, the impairment of the proper function of the vocal apparatus can also be caused in the process of the microvasculature complications of diabetes mellitus. This review was a comprehensive summary of the accessible literature concerning the influence of selected endocrinopathies on subjective and objective voice parameters. We analysed a total number of 16 English-language research papers from the PubMed database, released between 2008 and 2021, describing vocal changes in reproductive disorders such as polycystic ovary syndrome and congenital adrenal hyperplasia, thyroid disorders in shape of hypo- or hyperthyroidism and type 2 diabetes mellitus. The vast majority of the analysed articles proved some changes in voice in all mentioned conditions, although the detailed affected vocal parameters frequently differed between research. We assume that the main cause of the observed conflicting results might stem from non-homogeneous methodology designs of the analysed studies.
Voice disorders often remain undiagnosed. Many self-perceived questionnaires exist for various medical conditions. Here, we used the Greek Voice Handicap Index (VHI) to address the aforementioned problem. Everyone can fill in the VHI questionnaire and rate their symptoms easily. The innovative feature of this research is the global cut-off score calculated for the VHI. Therefore, the VHI is now capable of helping clinicians establish a more customizable treatment plan with the cut-off point identifying patients without normal phonation. For the purpose of finding the global cut-off point, a group of 180 participants was recruited in Greece (90 non-dysphonic participants and 90 with different types of dysphonia). The voice disordered group had higher VHI scores than those of the control group. In contrast to previous studies, we provided and validated for the first time the cut-off points for all VHI domains and, finally, a global cut-off point through ROC and precision-recall analysis in a voice disordered population. In practice, a score higher than the well-estimated global score indicates (without intervention) a possible voice disorder. Nevertheless, if the score is near the threshold, then the patient should definitely follow preventive measures.
Hyperfunctional voice disorders (HVDs) are the most common class of voice disorders, consisting of diagnoses such as vocal fold nodules and muscle tension dysphonia. These speech production disorders result in effort, fatigue, pain, and even complete loss of voice. The mechanisms underlying HVDs are largely unknown. Here, the auditory-motor control of voice fundamental frequency (fo) was examined in 62 speakers with and 62 speakers without HVDs. Due to the high prevalence of HVDs in singers, and the known impacts of singing experience on auditory-motor function, groups were matched for singing experience. Speakers completed three tasks, yielding: (1) auditory discrimination of voice fo; (2) reflexive responses to sudden fo shifts; and (3) adaptive responses to sustained fo shifts. Compared to controls, and regardless of singing experience, individuals with HVDs showed: (1) worse auditory discrimination; (2) comparable reflexive responses; and (3) a greater frequency of atypical adaptive responses. Atypical adaptive responses were associated with poorer auditory discrimination, directly implicating auditory function in this motor disorder. These findings motivate a paradigm shift for understanding development and treatment of HVDs.
Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.
Web-based health interventions are increasingly common and are promising for patients with voice disorders because web-based participation does not require voice use. To address needs such as Health Insurance Portability and Accountability Act compliance, unique user access, the ability to send automated reminders, and a limited development budget, we used the Research Electronic Data Capture (REDCap) data management platform to deliver a patient-facing psychological intervention designed for patients with voice disorders. This was a novel use of REDCap.
Menstruation-related hormonal alteration can be detrimental to the professional singing voice of women. Resonance Voice Therapy (RVT) has been proven to improve vocal production. However, no research to date has been conducted examining the subjective, acoustic, and stroboscopic effects of RVT on professional female singers having premenstrual or postmenopausal voice disorders.
In voice research and clinical assessment, many objective parameters are in use. However, there is no commonly used set of parameters that reflect certain voice disorders, such as functional dysphonia (FD); i.e. disorders with no visible anatomical changes. Hence, 358 high-speed videoendoscopy (HSV) recordings (159 normal females (NF), 101 FD females (FDF), 66 normal males (NM), 32 FD males (FDM)) were analyzed. We investigated 91 quantitative HSV parameters towards their significance. First, 25 highly correlated parameters were discarded. Second, further 54 parameters were discarded by using a LogitBoost decision stumps approach. This yielded a subset of 12 parameters sufficient to reflect functional dysphonia. These parameters separated groups NF vs. FDF and NM vs. FDM with fair accuracy of 0.745 or 0.768, respectively. Parameters solely computed from the changing glottal area waveform (1D-function called GAW) between the vocal folds were less important than parameters describing the oscillation characteristics along the vocal folds (2D-function called Phonovibrogram). Regularity of GAW phases and peak shape, harmonic structure and Phonovibrogram-based vocal fold open and closing angles were mainly important. This study showed the high degree of redundancy of HSV-voice-parameters but also affirms the need of multidimensional based assessment of clinical data.
When investigating voice disorders a series of processes are used when including voice screening and diagnosis. Both methods have limited standardized tests, which are affected by the clinician's experience and subjective judgment. Machine learning (ML) algorithms have been used as an objective tool in screening or diagnosing voice disorders. However, the effectiveness of ML algorithms in assessing and diagnosing voice disorders has not received sufficient scholarly attention.
The primary objective of the present systematic review is to: (1) identify the current vocal tasks being used for acoustic and/or auditory perceptual analysis to differentiate between individuals with and without voice disorders. The secondary objectives are to: (2) evaluate the evidence of the sensitivity, specificity and accuracy of those vocal tasks for acoustic and/or auditory perceptual analysis in discriminating the individuals with voice disorders from those without; and (3) compare the values between the vocal tasks in discriminating individuals with voice disorders from those without.
Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation.
Up to 80% of patients without a recurrent laryngeal nerve palsy report alteration in their voice after a thyroid procedure. The aims of this study were (1) to quantify voice changes after thyroid operation; (2) to correlate the changes to the extent of operation; and (3) to correlate voice changes to intraoperative recurrent laryngeal nerve swelling.
The objectives of this study were to identify the effects of smoking on the voice of smokers and present the baseline data for establishing the basis for preventing voice disorders. This study was evaluated using a meta-analysis from studies published between Jan 1, 2000, and Nov 15, 2018. As a result, the final meta-analysis was conducted using nine papers. The standard mean difference was analyzed after dividing the effects of smoking on voice into the pitch (F0), sound quality (jitter, shimmer, and noise to harmonic ratio; NHR), Maximum Phonation Time (MPT), and subjective voice problem. The results showed that there was a significant difference in F0 and MPT. On the other hand, the jitter, shimmer, NHR, and Voice Handicap Index (VHI) had different mean effect size but they were not significantly different. The analysis by sub-function of VHI results showed that the mean effect size was significantly different only in VHI-P (Physical). This study evaluated the effects of smoking on voice using meta-analysis. It was confirmed that smoking had significant and moderate effects on the F0 of voice, MPT, VHI, and physical functions. It is necessary for future meta-analysis studies to conduct randomized controlled experiments or longitudinal studies to confirm the effect sizes of variables.
Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder. The present study aimed at investigating (1) voice features collected during phone calls as objective markers of affective states in bipolar disorder and (2) if combining voice features with automatically generated objective smartphone data on behavioral activities (for example, number of text messages and phone calls per day) and electronic self-monitored data (mood) on illness activity would increase the accuracy as a marker of affective states. Using smartphones, voice features, automatically generated objective smartphone data on behavioral activities and electronic self-monitored data were collected from 28 outpatients with bipolar disorder in naturalistic settings on a daily basis during a period of 12 weeks. Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale, respectively, by a researcher blinded to smartphone data. Data were analyzed using random forest algorithms. Affective states were classified using voice features extracted during everyday life phone calls. Voice features were found to be more accurate, sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states. Combining voice features with automatically generated objective smartphone data on behavioral activities and electronic self-monitored data increased the accuracy, sensitivity and specificity of classification of affective states slightly. Voice features collected in naturalistic settings using smartphones may be used as objective state markers in patients with bipolar disorder.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count: