Academic literature on the topic 'Disordered voice database'

Purpose: Signal typing has been used to categorize healthy and disordered voices; however, human voices are likely comprised of differing proportions of periodic type 1 elements, type 2 elements that are periodic with modulations, aperiodic type 3 elements, and stochastic type 4 elements. A novel diffusive chaos method is presented to detect the distribution of voice types within a signal with the goal of providing an objective and clinically useful tool for evaluating the voice. It was predicted that continuous calculation of the diffusive chaos parameter throughout the voice sample would allow for construction of comprehensive voice type component profiles (VTCP). Methods: One hundred thirty-five voice samples of sustained /a/ vowels were randomly selected from the Disordered Voice Database Model 4337. All samples were classified according to the voice type paradigm using spectrogram analysis, yielding 34 type 1, 35 type 2, 42 type 3, and 24 type 4 voice samples. All samples were then analyzed using the diffusive chaos method, and VTCPs were generated to show the distribution of the 4 voice type components (VTC). Results: The proportions of VTC1 varied significantly between the majority of the traditional voice types ( P < .001). Three of the 4 VTCs of type 3 voices were significantly different from the VTCs of type 4 voices ( P < .001). These results were compared to calculations of spectrum convergence ratio, which did not vary significantly between voice types 1 and 2 or 2 and 3. Conclusion: The diffusive chaos method demonstrates proficiency in generating comprehensive VTCPs for disordered voices with varying severity. In contrast to acoustic parameters that provide a single measure of disorder, VTCPs can be used to detect subtler changes by observing variations in each VTC over time. This method also provides the advantage of quantifying stochastic noise components that are due to breathiness in the voice.

The purpose of this study was to determine the extent to which other communicative disorders coexist with voice disorders in school children. The authors randomly selected two voice deviant groups and a control group from a database of nearly 39,000 school children in grades 1–12. Hoarseness was the most commonly occurring voice disorder in both groups. The majority of voice disordered children had coexisting articulation deviations. In addition, the voice disordered samples differed significantly from controls on two language measures and mean pure-tone hearing thresholds. This study supports other research indicating that different communication disorders frequently coexist.

Purpose: The overall aim of this study was to apply local intrinsic dimension ( Di) estimation to quantify high-dimensional, disordered voice and discriminate between the 4 types of voice signals. It was predicted that continuous Di analysis throughout the entire time-series would generate comprehensive descriptions of voice signal components, called voice type component profiles (VTCP), that effectively distinguish between the 4 voice types. Method: One hundred thirty-five voice recording samples of the sustained vowel /a/ were obtained from the Disordered Voice Database Model 4337 and spectrographically classified into the voice type paradigm. The Di and correlation dimension ( D2) were then used to objectively analyze the voice samples and compared based on voice type differentiation efficacy. Results: The D2 exhibited limited effectiveness in distinguishing between the 4 voice type signals. For Di analysis, significant differences were primarily observed when comparing voice type component 1 (VTC1) and 4 (VTC4) across the 4 voice type signals ( P < .001). The 4 voice type components (VTCs) significantly differentiated between low-dimensional, type 3 and high-dimensional, type 4 signals ( P < .001). Conclusions: The Di demonstrated improvements over D2 in 2 distinct manners: enhanced resolution at high data dimensions and comprehensive description of voice signal elements.

Objectives To examine the relationship between community-acquired pneumonia (CAP) and proton pump inhibitor (PPI) treatment among patients with laryngeal/voice disorders. Study Design Retrospective cohort analysis. Setting Large national administrative US claims database. Subjects and Methods Patients were included if they were ≥18 years old; had outpatient treatment for a laryngeal/voice disorder from January 1, 2010, to December 31, 2014 (per International Classification of Diseases, Ninth Revision, Clinical Modification codes); had 12 months of continuous enrollment prior to the index date (ie, first diagnosis of laryngeal/voice disorder); had no preindex diagnosis of CAP; and had prescription claims captured from 1 year preindex to end of follow-up. Patient demographics, comorbid conditions, index laryngeal diagnosis, number of unique preindex patient encounters, and CAP diagnoses during the postindex 3 years were collected. Two models—a time-dependent Cox regression model and a propensity score–based approach with a marginal structural model—were separately performed for patients with and without pre–index date PPI prescriptions. Results A total of 392,355 unique patients met inclusion criteria; 188,128 (47.9%) had a PPI prescription. The 3-year absolute risk for CAP was 4.0% and 5.3% among patients without and with preindex PPI use, respectively. For patients without and with pre–index date PPI use, the CAP occurrence for a person who had already received a PPI is 30% to 50% higher, respectively, than for a person who had not yet had a PPI but may receive one later. Conclusions Patients without and with pre–index date PPI use experienced a roughly 30% to 50% increased likelihood of CAP, respectively, as compared with patients who had not had PPI prescriptions.

Tato disertační práce je zaměřena na analýzu řečového signálu za učelem detekce nemocí ovlivňujících strukturu hlasových orgánů, obzvláště těch, které mění strukturální character hlasivek. Poskytnut je přehled současných technik. Dále jsou popsány zdroje použitých nahrávek pro zdravé a nemocné mlučí. Hlavním učelem této disertační práce je popsat vypočetní postup k odhadu parametrů modelu hlasového zdroje, které umožní následnou detekci a klasifikaci nemocí hlasivek. Poskytujeme detailní popis analýzy řečových signálů, které mohou být odvozeny z parametrických modelů hlasivek.

"The study of the peculiarities of speech of children with atypical development is necessary for the development of educational programs, children’s socialization and adaptation in society. The aim of this study is to determine the acoustic features of voice and speech of children with autism spectrum disorders (ASD) as a possible additional diagnostic criterion. The multiplicity of symptomatology, different age of its manifestation, and the presence of a leading symptom complex individually for each child make it difficult to diagnose ASD. To determine the specificity of speech features of ASD, we analyzed the speech of children with developmental disabilities in which speech disorders accompany the disease - Down syndrome (DS), intellectual disabilities (ID), mixed specific developmental disorders (MDD). The features that reflect the main physiological processes occurring in the speech tract during voice and speech production are selected for analysis. The speech of 300 children aged 4-16 years was analyzed. Speech files are selected from the speech database ""AD_Child.Ru"" (Lyakso et al., 2019). Acoustic features of voice and speech, which are specific for different developmental disorders, were determined. The speech of ASD children is characterized by: high pitch values (high voice); pitch variability; high values for the third formant (emotional) and its intensity causing ""atypical"" spectrogram of the speech signal; high values of vowel articulation index (VAI). The speech of children with DS is characterized by the maximal duration of vowels in words; low pitch values (low voice); a wide range of values of the VAI depending on the difficulty of speech material; low values of the third formant; unformed most of consonant phonemes. The characteristics of speech of children with ID are: high values of vowel’s duration in words, the pitch, and the third formant, low values of the VAI; of MDD - low pitch values and high values of the VAI. Based on the identified peculiarities specific to each disease, the set of acoustic features specific to ASD can be considered as a biomarker of autism and used as an additional diagnostic criterion. This will allow a timely diagnose, appoint treatment and develop individual programs for children. Speech characteristics of children with ID, DS, and MDD can be considered to a greater extent in the training and socialization of children and used in the development of training programs taking into account individual peculiarities of children."