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Background: A challenge for clinicians and researchers in laryngology is a lack of international consensus for an agreed framework to classify homogenous groups of voice disorders. Consistency in terminology and agreement in how conditions are classified will provide greater clarity for clinicians and researchers., Objective: This scoping review aimed to examine the published literature on frameworks, terminology, and criteria for the classification of voice disorders., Design: Seven online databases (MEDLINE, Embase, CINAHL, PsycInfo, Scopus, Cochrane Collaboration, Web of Science) and grey literature sources were searched. Studies published from 1940 to 2021 were included if they provided a descriptive detail of a classification framework structure and described the methodological approaches to determine classification. A narrative synthesis of the main concepts including terminology, classification criteria, grouping of conditions, critical appraisal items and gaps in research was undertaken., Results: A total of 2,675 publications were screened. Twenty sources met inclusion criteria, including published articles and grey literature. Thirty-five classification groups and over 150 sub-groups were described. The classification group labels, and criteria for inclusion of conditions varied across the frameworks. Several key themes in terminology and criteria useful for classification are discussed, and a core set of suggested terms and definitions are presented., Conclusions: The quality of research on classification frameworks for voice disorders is low and not one system encompasses all voice disorders across the whole spectrum. Continued high quality research using consensus methodology and inter-rater reliability scores is recommended to develop and test an internationally agreed classification framework for voice disorders., Competing Interests: Declarations of competing interest None, (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryngeal cancer is challenging, especially when differentiating it from other laryngeal ailments. This study presents an artificial intelligence model designed to distinguish between healthy voices, laryngeal cancer voices, and those of the other laryngeal conditions. We gathered voice samples of individuals with laryngeal cancer, vocal cord paralysis, benign mucosal diseases, and healthy participants. Comprehensive testing was conducted to determine the best mel-frequency cepstral coefficient conversion and machine learning techniques, with results analyzed in-depth. In our tests, laryngeal diseases distinguishing from healthy voices achieved an accuracy of 0.85-0.97. However, when multiclass classification, accuracy ranged from 0.75 to 0.83. These findings highlight the challenges of artificial intelligence-driven voice-based diagnosis due to overlaps with benign conditions but also underscore its potential., (© 2024. The Author(s).)

The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice pathology disorders can be effectively detected using Artificial Intelligence and computer-aided voice pathology classification tools. Previous studies focused primarily on binary classification, leaving limited attention to multi-class classification. This study proposes three different neural network architectures to investigate the feature characteristics of three voice pathologies-Hyperkinetic Dysphonia, Hypokinetic Dysphonia, Reflux Laryngitis, and healthy voices using multi-class classification and the Voice ICar fEDerico II (VOICED) dataset. The study proposes UNet++ autoencoder-based denoiser techniques for accurate feature extraction to overcome noisy data. The architectures include a Multi-Layer Perceptron (MLP) trained on structured feature sets, a Short-Time Fourier Transform (STFT) model, and a Mel-Frequency Cepstral Coefficients (MFCC) model. The MLP model on 143 features achieved 97.1% accuracy, while the STFT model showed similar performance with increased sensitivity of 99.8%. The MFCC model maintained 97.1% accuracy but with a smaller model size and improved accuracy on the Reflux Laryngitis class. The study identifies crucial features through saliency analysis and reveals that detecting voice abnormalities requires the identification of regions of inaudible high-pitch sounds. Additionally, the study highlights the challenges posed by limited and disjointed pathological voice databases and proposes solutions for enhancing the performance of voice abnormality classification. Overall, the study's findings have potential applications in clinical applications and specialized audio-capturing tools., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Highlights: [•] Early voice pathology diagnosis is very worthwhile to prevent suffering from chronic voice diseases. [•] This study introduces an efficient algorithm for classification of pathologic voices. [•] The proposed algorithm benefits from the ability of energy or Shannon entropy features that are extracted from level eighth of wavelet packet tree. [•] Significant classification results are achieved by applying multi-class linear discriminant analysis. [ABSTRACT FROM AUTHOR]

Importance: Prevalent schemes that have been used for arranging voice pathologies have shaped theoretical and clinical views and the conceptualization of the pathologies and of the field as a whole. However, these available schemes contain inconsistencies and categorical overlaps., Objective: To develop and evaluate a new approach for arranging voice pathologies, using 2 continuous scales, organicity and tonicity, which were used to construct a 2-dimensional plane., Design, Setting, and Participants: This survey study was conducted among experts in the fields of laryngology and/or voice disorders from 10 countries. The survey was conducted using an online platform from March to May 2021. The data were analyzed in June 2021. Of the 45 experts who were initially approached, 39 (86.7%) completed the survey., Main Outcomes and Measures: The primary outcome measures were group ratings on 2 rating scales: organicity and tonicity. On the organicity scale, 0 represented nonorganic and 10 organic. On the tonicity scale, 0 represented hypotonic and 10 hypertonic., Results: Participants included 16 laryngologists and 23 speech-language pathologists, of whom 27 (69.2%) were women and 12 (30.8%) men with a mean age of 55 years. The Cronbach α was high for organicity and tonicity (0.98 and 0.97, respectively). Interrater agreement (rwg) was moderate to very strong (rwg≥0.50) for most pathologies. The correlation between the 2 scales was moderate and negative (r = -0.38; P = .03). The pathologies were scattered across the full range of both scales and the 4 quadrants of the 2-dimensional plane, suggesting the continuity and bidimensionality of the new arrangement scheme. In addition, a latent profile analysis suggested that the 4-cluster solution is valid and roughly corresponded to the 4 quadrants of the constructed plane., Conclusions and Relevance: The findings of this survey study suggest the potential use of a 2-dimensional plane that was based on 2 continuous scales as a new arrangement scheme for voice disorders. The results suggest that this approach provides a valid representation of the field based on 2 basic measures beyond the specific etiology of each laryngeal pathology or condition. This simple and comprehensive organization scheme has the potential to facilitate new insights on the nature of voice pathologies, considering the interpathology similarities and differences.

BACKGROUND: Although the understanding of hypobaric hypoxia is increasing, it remains a hazard in aviation medicine. This study examined the feasibility of detecting voice markers sensitive to acute hypobaric hypoxia in an early presymptomatic (PRE-SYMP) stage. METHOD: Eight subjects qualified with hypobaric training completed a series of standardized speech tests in a hypobaric chamber at 20,000 ft and 25,000 ft (6096 and 7620 m) of altitude. Voice response patterns were analyzed in terms of fundamental frequency (F0), F0 range, and voice onset time (VOT). We hypothesized a PRE-SYMP compensatory stage in voice reactivity. RESULTS: There was a different dose-response reactivity course at 20,000 ft vs. 25,000 ft, nonlinear to altitude. At 20,000 ft, our hypothesis was confirmed. In comparison to sea level, a PRE-SYMP compensatory stage could be distinguished, characterized by a decreased F0 range, decreased VOT, and increased F0. During a transitional (TRANS) stage, in comparison with sea level, the F0-range reset, VOT decreased, and F0 increased. During a symptomatic (SYMP) stage, F0 increased, F0 range increased, and VOT decreased. At 25,000 ft, in comparison to sea level, voice reactivity showed increased F0 and F0 range and decreased VOT in a PRE-SYMP stage and increased F0 and F0 range in the SYMP stage. DISCUSSION: The compensatory PRE-SYMP stage is suggested to be the expression of ongoing bottom-up and top-down regulatory mechanisms, whereas the 25,000-ft results are interpreted as a combination of tonic and phasic voice reactivity. This tonic component needs to be foreseen in sea level baseline measures. Van Puyvelde M, Neyt X, Vanderlinden W, Van den Bossche M, Bucovaz T, De Winne T, Pattyn N. Voice reactivity as a response to acute hypobaric hypoxia at high altitude . Aerosp Med Hum Perform. 2020; 91(6):471-478.

Purpose: Describe the self-referred personal behavior profiles of university professors and verify the association of these profiles with the self-assessment of communicative aspects and vocal symptoms., Methods: Study conducted with 334 professors at a public university who responded to an online questionnaire regarding voice use in teaching practice. Personal behavior profile classification was the response variable, which was divided into four types: pragmatic, analytical, expressive and affable. Explanatory variables were vocal self-perception, vocal resources, and communicative aspects. Descriptive data analysis was performed with application of the Pearson's Chi-squared and Fisher's Exact tests., Results: University professors identified themselves more with the affable and expressive personal behavior profiles. Overall, professors presented good self-perception about vocal and communicative aspects, in addition to having reported few vocal symptoms. Profiles differed for some of the assessed variables, namely, pragmatic professors reported high speech velocity and sporadic eye contact; expressive professors demonstrated self-perception about their voice and strong voice intensity; those in the analytical profile self-reported negative perception about vocal quality, weak voice intensity, poor articulation and rapid speaking rate; the other professors mostly reported voice tiredness symptoms and difficulty projecting the voice., Conclusion: University professors identify themselves mostly with the affable and expressive profiles. Self-perception analysis of the personal behavior profile in university professors showed the influence of self-reported personality characteristics on communicative skills in the classroom.

Background: The consensus statement by the Task Force on Tremor of the International Parkinson and Movement Disorder Society excludes individuals with "isolated voice tremor" as a clinical variant of essential tremor (ET). This clinical viewpoint presents a rationale for reconsideration of "isolated voice tremor" as a clinical variant of ET., Methods: Evidence from the literature was extracted to characterize the clinical phenotype of "isolated voice tremor," or essential vocal tremor (EVT). Clinical features were extracted from relevant literature available at pubmed.gov using the terms "EVT," "essential voice tremor," "primary voice tremor," and "organic voice tremor.", Results: The average age of onset in those with EVT was older than 60 years (range 19-84 years), with 75-93% being female. The typical duration of vocal tremor ranged from 1 to 13 years (average 6 years). The distribution of structures exhibiting tremor included the larynx, soft palate, pharynx, and base of tongue in the majority of patients, with some exhibiting tremor of the head and respiratory musculature. The condition of tremor occurred during speech and quiet respiration in 74% of individuals. Rate of tremor ranged from 4 to 10 Hz. Nearly 70% reported onset of vocal tremor prior to upper limb involvement. Family history of tremor was reported in 38-42% of individuals., Discussion: Those previously classified with EVT demonstrate a similar familial history, rate, tremor classification, and body distribution of ET. EVT is proposed as a clinical variant of ET in the pattern of onset and progression of body distribution from the midline cranial to spinal neural pathways., Competing Interests: Funding: This work is funded by the NIDCD to conduct research on those with essential tremor and vocal tremor (R01DC016838, Physiologic correlates of vocal tremor affecting those with essential tremor, PI: Barkmeier-Kraemer). Conflict of Interest: The author conducts funded and unfunded research on individuals with vocal tremor relevant to the topic of this viewpoint. Ethics Statement: Not applicable for this category of article., (© 2020 Barkmeier-Kraemer.)

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 VTC 1 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.

Background: Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems., Summary: We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context. Key Messages: The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most cohorts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model., (© 2020 S. Karger AG, Basel.)

Importance: A roadblock for research on adductor spasmodic dysphonia (ADSD), abductor SD (ABSD), voice tremor (VT), and muscular tension dysphonia (MTD) is the lack of criteria for selecting patients with these disorders., Objective: To determine the agreement among experts not using standard guidelines to classify patients with ABSD, ADSD, VT, and MTD, and develop expert consensus attributes for classifying patients for research., Design, Setting and Participants: From 2011 to 2016, a multicenter observational study examined agreement among blinded experts when classifying patients with ADSD, ABSD, VT or MTD (first study). Subsequently, a 4-stage Delphi method study used reiterative stages of review by an expert panel and 46 community experts to develop consensus on attributes to be used for classifying patients with the 4 disorders (second study). The study used a convenience sample of 178 patients clinically diagnosed with ADSD, ABSD, VT MTD, vocal fold paresis/paralysis, psychogenic voice disorders, or hypophonia secondary to Parkinson disease. Participants were aged 18 years or older, without laryngeal structural disease or surgery for ADSD and underwent speech and nasolaryngoscopy video recordings following a standard protocol., Exposures: Speech and nasolaryngoscopy video recordings following a standard protocol., Main Outcomes and Measures: Specialists at 4 sites classified 178 patients into 11 categories. Four international experts independently classified 75 patients using the same categories without guidelines after viewing speech and nasolaryngoscopy video recordings. Each member from the 4 sites also classified 50 patients from other sites after viewing video clips of voice/laryngeal tasks. Interrater κ less than 0.40 indicated poor classification agreement among rater pairs and across recruiting sites. Consequently, a Delphi panel of 13 experts identified and ranked speech and laryngeal movement attributes for classifying ADSD, ABSD, VT, and MTD, which were reviewed by 46 community specialists. Based on the median attribute rankings, a final attribute list was created for each disorder., Results: When classifying patients without guidelines, raters differed in their classification distributions (likelihood ratio, χ2 = 107.66), had poor interrater agreement, and poor agreement with site categories. For 11 categories, the highest agreement was 34%, with no κ values greater than 0.26. In external rater pairs, the highest κ was 0.23 and the highest agreement was 38.5%. Using 6 categories, the highest percent agreement was 73.3% and the highest κ was 0.40. The Delphi method yielded 18 attributes for classifying disorders from speech and nasolaryngoscopic examinations., Conclusions and Relevance: Specialists without guidelines had poor agreement when classifying patients for research, leading to a Delphi-based development of the Spasmodic Dysphonia Attributes Inventory for classifying patients with ADSD, ABSD, VT, and MTD for research.

Objectives: This study aimed to assess potential associations between self-reported symptoms of laryngopharyngeal reflux (LPR) and voice disorders among two undiagnosed cohorts in Saudi Arabia., Methods: This cross-sectional study was conducted from February to April 2017 in Khobar, Saudi Arabia. Validated Arabic versions of the Reflux Symptom Index (RSI) and 10-item Voice Handicap Index (VHI-10) were distributed to 400 teachers at 13 schools and 300 members of the general population attending an ear, nose and throat clinic in Khobar. Scores of >13 and >11 on the RSI and VHI-10 indicated a potential subjective diagnosis of LPR and voice disorders, respectively., Results: A total of 446 individuals took part in the study, including 260 members of the general population (response rate: 86.7%) and 186 teachers (response rate: 46.5%). The mean age was 32.5 years. In total, 62.2% complained of voice and/or reflux problems, with the remaining 37.8% not reporting/unaware of any problems in this regard. Among the teachers, 30.6% and 18.3% had positive RSI and VHI-10 scores, respectively, while 43.1% and 14.6% of the individuals from the general population had positive RSI and VHI-10 scores, respectively. Overall, VHI-10 scores were significantly associated with RSI scores ( P <0.001)., Conclusion: A significant association between RSI and VHI-10 scores suggests that there may be an association between LPR and voice disorders. These tools would therefore be a valuable method of monitoring patients; however, they cannot be used to confirm a diagnosis. Thus, more detailed studies are needed to confirm this association using a larger sample size., Competing Interests: CONFLICT OF INTEREST The authors declare no conflicts of interest.

Purpose: The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression., Study Design: Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100 Monte Carlo experiments were applied to analyze the output of jitter, shimmer, correlation dimension, and spectrum convergence ratio. The computational output of the 4 classifiers was then plotted against signal chaos level to investigate the performance of these acoustic analysis methods under varying degrees of signal chaos., Method: A diffusive behavior detection-based chaos level test was used to investigate the performances of different voice classification methods. Voice signals were constructed by varying the signal-to-noise ratio to establish differing signal chaos conditions., Results: Chaos level increased sigmoidally with increasing noise power. Jitter and shimmer performed optimally when the chaos level was less than or equal to 0.01, whereas correlation dimension was capable of analyzing signals with chaos levels of less than or equal to 0.0179. Spectrum convergence ratio demonstrated proficiency in analyzing voice signals with all chaos levels investigated in this study., Conclusion: The results of this study corroborate the performance relationships observed in previous studies and, therefore, demonstrate the validity of the validation test method. The presented chaos level validation test could be broadly utilized to evaluate acoustic analysis methods and establish the most appropriate methodology for objective voice analysis in clinical practice.

Background: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification., Methods: We recruited 100 patients with glottic neoplasm, 508 with phonotraumatic lesions, and 153 with unilateral vocal palsy. Statistical analyses revealed significantly different distributions of demographic and symptomatic variables. Machine learning algorithms, including decision tree, linear discriminant analysis, K-nearest neighbors, support vector machine, and artificial neural network, were applied to classify voice disorders., Results: The results showed that demographic features were more effective for detecting neoplastic and phonotraumatic lesions, whereas symptoms were useful for detecting vocal palsy. When combining demographic and symptomatic variables, the artificial neural network achieved the highest accuracy of 83 ± 1.58%, whereas the accuracy achieved by other algorithms ranged from 74 to 82.6%. Decision tree analyses revealed that sex, age, smoking status, sudden onset of dysphonia, and 10-item voice handicap index scores were significant characteristics for classification., Conclusion: This study demonstrated a significant difference in demographic and symptomatic features between glottic neoplasm, phonotraumatic lesions, and vocal palsy. These features may facilitate automatic classification of voice disorders through machine learning algorithms., (© 2018 S. Karger AG, Basel.)

Objective: To verify the correlation between vocal tract discomfort symptoms and perceived voice handicaps in gospel singers, analyzing possible differences according to gender., Methods: 100 gospel singers volunteered, 50 male and 50 female. All participants answered two questionnaires: Vocal Tract Discomfort (VTD) scale and the Modern Singing Handicap Index (MSHI) that investigates the vocal handicap perceived by singers, linking the results of both instruments (p<0.05)., Results: Women presented more perceived handicaps and also more frequent and higher intensity vocal tract discomfort. Furthermore, the more frequent and intense the vocal tract symptoms, the higher the vocal handicap for singing., Conclusion: Female gospel singers present higher frequency and intensity of vocal tract discomfort symptoms, as well as higher voice handicap for singing than male gospel singers. The higher the frequency and intensity of the laryngeal symptoms, the higher the vocal handicap will be.

Objectives: Development of a noninvasive method for separating different vocal fold diseases is an important issue concerning vocal analysis. Due to the time variations along a pathologic vocal signal, application of dynamic pattern modeling tools is expected to help in the detection of defects that occur in the speech production mechanism., Materials and Methods: In the present study, the hidden Markov model, which is a state space model, is employed to sort some of the vocal diseases. Moreover, this research mainly investigates the effects of the processed vocal signal lengths on the mentioned sorting task. To this end, the signal lengths of 1, 3, and 5 seconds of different disorders are used., Results: The experimental results show that some pathologic conditions in vocal folds such as cyst, false vocal cord, and mass are more evident in continued voice production, and the recognition accuracies gained via dynamic modeling of pathologic voice signals with more lengths are considerably improved., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: Speech signal processing techniques have provided several contributions to pathologic voice identification, in which healthy and unhealthy voice samples are evaluated. A less common approach is to identify laryngeal pathologies, for which the use of a noninvasive method for pathologic voice identification is an important step forward for preliminary diagnosis. In this study, a hierarchical classifier and a combination of systems are used to improve the accuracy of a three-class identification system (healthy, physiological larynx pathologies, and neuromuscular larynx pathologies)., Method: Three main subject classes were considered: subjects with physiological larynx pathologies (vocal fold nodules and edemas: 59 samples), subjects with neuromuscular larynx pathologies (unilateral vocal fold paralysis: 59 samples), and healthy subjects (36 samples). The variables used in this study were a speech task (sustained vowel /a/ or continuous reading speech), features with or without perceptual information, and features with or without direct information about formants evaluated using single classifiers. A hierarchical classification system was designed based on this information., Results: The resulting system combines an analysis of continuous speech by way of the commonly used sustained vowel /a/ to obtain spectral and perceptual speech features. It achieved an accuracy of 84.4%, which represents an improvement of approximately 9% compared with the stand-alone approach. For pathologic voice identification, the accuracy obtained was 98.7%, and the identification accuracy for the two pathology classes was 81.3%., Conclusions: Hierarchical classification and system combination create significant benefits and introduce a modular approach to the classification of larynx pathologies., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Background and Objective: Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes., Materials and Methods: Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples., Results: The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: The purposes of this literature review were (1) to identify and assess frameworks for clinical characterization of episodic laryngeal breathing disorders (ELBD) and their subtypes, (2) to integrate concepts from these frameworks into a novel theoretical paradigm, and (3) to provide a preliminary algorithm to classify clinical features of ELBD for future study of its clinical manifestations and underlying pathophysiological mechanisms., Study Design: This is a literature review., Methods: Peer-reviewed literature from 1983 to 2015 pertaining to models for ELBD was searched using Pubmed, Ovid, Proquest, Cochrane Database of Systematic Reviews, and Google Scholar. Theoretical models for ELBD were identified, evaluated, and integrated into a novel comprehensive framework. Consensus across three salient models provided a working definition and inclusionary criteria for ELBD within the new framework. Inconsistencies and discrepancies within the models provided an analytic platform for future research., Results: Comparison among three conceptual models-(1) Irritable larynx syndrome, (2) Dichotomous triggers, and (3) Periodic occurrence of laryngeal obstruction-showed that the models uniformly consider ELBD to involve episodic laryngeal obstruction causing dyspnea. The models differed in their description of source of dyspnea, in their inclusion of corollary behaviors, in their inclusion of other laryngeal-based behaviors (eg, cough), and types of triggers., Conclusion: The proposed integrated theoretical framework for ELBD provides a preliminary systematic platform for the identification of key clinical feature patterns indicative of ELBD and associated clinical subgroups. This algorithmic paradigm should evolve with better understanding of this spectrum of disorders and its underlying pathophysiological mechanisms., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Objective/hypothesis: The purpose of this paper is to introduce the rate of divergence as an objective measure to differentiate between the four voice types based on the amount of disorder present in a signal. We hypothesized that rate of divergence would provide an objective measure that can quantify all four voice types., Study Design: A total of 150 acoustic voice recordings were randomly selected and analyzed using traditional perturbation, nonlinear, and rate of divergence analysis methods., Methods: We developed a new parameter, rate of divergence, which uses a modified version of Wolf's algorithm for calculating Lyapunov exponents of a system. The outcome of this calculation is not a Lyapunov exponent, but rather a description of the divergence of two nearby data points for the next three points in the time series, followed in three time-delayed embedding dimensions. This measure was compared to currently existing perturbation and nonlinear dynamic methods of distinguishing between voice signals., Results: There was a direct relationship between voice type and rate of divergence. This calculation is especially effective at differentiating between type 3 and type 4 voices (P < 0.001) and is equally effective at differentiating type 1, type 2, and type 3 signals as currently existing methods., Conclusion: The rate of divergence calculation introduced is an objective measure that can be used to distinguish between all four voice types based on the amount of disorder present, leading to quicker and more accurate voice typing as well as an improved understanding of the nonlinear dynamics involved in phonation., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives and Background: Automatic voice pathology detection and classification systems effectively contribute to the assessment of voice disorders, which helps clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. This work concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using correlation functions. In this paper, we extracted maximum peak values and their corresponding lag values from each frame of a voiced signal by using correlation functions as features to detect and classify pathological samples. These features are investigated in different frequency bands to see the contribution of each band on the detection and classification processes., Material and Methods: Various samples of sustained vowel /a/ of normal and pathological voices were extracted from three different databases: English, German, and Arabic. A support vector machine was used as a classifier. We also performed a t test to investigate the significant differences in mean of normal and pathological samples., Results: The best achieved accuracies in both detection and classification were varied depending on the band, the correlation function, and the database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. In detection, the highest acquired accuracies when using cross-correlation were 99.809%, 90.979%, and 91.168% in the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and Arabic Voice Pathology Database databases, respectively. However, in classification, the highest acquired accuracies when using cross-correlation were 99.255%, 98.941%, and 95.188% in the three databases, respectively., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Background and Objective: Automatic voice pathology detection using sustained vowels has been widely explored. Because of the stationary nature of the speech waveform, pathology detection with a sustained vowel is a comparatively easier task than that using a running speech. Some disorder detection systems with running speech have also been developed, although most of them are based on a voice activity detection (VAD), that is, itself a challenging task. Pathology detection with running speech needs more investigation, and systems with good accuracy (ACC) are required. Furthermore, pathology classification systems with running speech have not received any attention from the research community. In this article, automatic pathology detection and classification systems are developed using text-dependent running speech without adding a VAD module., Method: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision., Results: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems., Discussion: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples., (Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

The classification of voice diseases has many applications in health, in diseases treatment, and in the design of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses the parameters of the glottal signal to help the identification of two types of voice disorders related to the pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained through a known inverse filtering method, and they are used as inputs to an Artificial Neural Network, a Support Vector Machine, and also to a Hidden Markov Model, to obtain the classification, and to compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds; speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule or unilateral paralysis present in the vocal folds. The database is composed of 248 voice recordings (signals of vowels production) containing samples corresponding to the three groups mentioned. In this study, a larger database was used for the classification when compared with similar studies, and its classification rate is superior to other studies, reaching 97.2%., (Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: The two goals of the present study were to (1) determine the ability of commonly used aerodynamic voice measures to capture change as a function of known interventions and (2) determine if certain aerodynamic measures demonstrate better responsiveness to change in specific disorder types than others., Study Design: This is a retrospective, longitudinal, single-blinded, cross-sectional study., Methods: Patients (n = 70) with a single voice disorder diagnosis of benign vocal fold lesions (lesions), unilateral vocal fold paralysis (UVFP), primary muscle tension dysphonia (MTD-1), or vocal fold atrophy (atrophy) underwent baseline testing, a single intervention (phonosurgery or voice therapy), and follow-up testing. Common aerodynamic measurements were completed in repeated syllables and an all-voiced sentence., Results: Statistically significant improvements were observed for two outcome measures, average airflow in syllables, and average airflow in the all-voiced sentence. Patients with lesions, UVFP, and MTD-1 improved in average airflow in the all-voiced sentence. Patients with UVFP also improved in airflow in syllables., Conclusions: Average airflow in the all-voiced sentence changed as a function of treatment for the lesion, MTD-1, and UVFP groups, demonstrating a disorder-specific pattern. Laryngeal airway resistance, and estimates of average subglottal pressure did not show significant change. Average airflow in the all-voiced sentence measurements is recommended as a routine voice measure, and further investigation of other aerodynamic measures' sensitivity to change is warranted., (Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Purpose: To investigate whether there is association between vocal symptoms, voice complaint, and working and voice conditions self-reported by telemarketers., Methods: Study participants were 72 telemarketing operators, both genders, who work in an emergency call center. They responded to a questionnaire on personal data, voice complaints, vocal symptoms, and working conditions. Data analysis was performed using the Chi-square, Mann-Whitney, and Fisher's Exact tests., Results: Correlation was found between the telemarketers in the study group and voice disorder complaint for aspects of working condition, such as noisy working environment, and aspects of voice condition, such as change in the voice and workplace absence. Differences were observed between the mean of auditory vocal symptoms of participants for echo in the work room, change in voice, and workplace absence, as well as between the mean of their sensory vocal symptoms in relation to the variables stressful work rate, noisy working environment, noise from other rooms, echo in the work room, radio use, change in voice, and workplace absence., Conclusion: Correlation was found between the presence of vocal complaints, number of auditory and sensory vocal symptoms, and working conditions self-reported by the telemarketers surveyed.

Purpose: To compare the responses related to vocal symptoms in two versions of the Vocal Production Condition - Teacher (CPV-T) questionnaire, with responses on a Likert scale and a Visual Analog Scale (VAS), in order to evaluate which is the best measurement method., Methods: A cross-sectional observational study was conducted with teachers with voice disorders during the period from July 2011 to July 2012. All teachers answered the CPV-T in two versions: with answers on a 4-point Likert scale and on a 50-mm VAS. The answers related to vocal symptoms dimension were analyzed., Results: Most of the symptoms showed good (hoarseness, high-pitched voice, unstable voice, weak voice, effort when speaking, throat clearing, burning throat, and pain when speaking) or regular concordance (loss of voice, failing voice, low-pitched voice, vocal fatigue, dry throat, lump in the throat, secretion in the throat, pain when swallowing, difficulty swallowing, and dry cough)., Conclusion: The CPV-T questionnaire with answers on Likert scale proved to be more suitable than the VAS owing to the ease of understanding and interpretation, in addition to facilitating the input of answers for the researcher. Therefore, the Likert scale was chosen for the CPV-T, considering it to be validated as the method to measure the answers. The dimension of vocal aspects evaluated in the present study, the Voice Disorder Screening Index (ITDV), can be used in epidemiological studies to estimate the prevalence of vocal symptoms and in the Speech-Language Pathology and Audiology clinic routine or in monitoring teachers throughout their careers.

This paper evaluates the accuracy of different characterization methods for the automatic detection of multiple speech disorders. The speech impairments considered include dysphonia in people with Parkinson's disease (PD), dysphonia diagnosed in patients with different laryngeal pathologies (LP), and hypernasality in children with cleft lip and palate (CLP). Four different methods are applied to analyze the voice signals including noise content measures, spectral-cepstral modeling, nonlinear features, and measurements to quantify the stability of the fundamental frequency. These measures are tested in six databases: three with recordings of PD patients, two with patients with LP, and one with children with CLP. The abnormal vibration of the vocal folds observed in PD patients and in people with LP is modeled using the stability measures with accuracies ranging from 81% to 99% depending on the pathology. The spectral-cepstral features are used in this paper to model the voice spectrum with special emphasis around the first two formants. These measures exhibit accuracies ranging from 95% to 99% in the automatic detection of hypernasal voices, which confirms the presence of changes in the speech spectrum due to hypernasality. Noise measures suitably discriminate between dysphonic and healthy voices in both databases with speakers suffering from LP. The results obtained in this study suggest that it is not suitable to use every kind of features to model all of the voice pathologies; conversely, it is necessary to study the physiology of each impairment to choose the most appropriate set of features.

We present a system for classification of bilateral vocal fold mobility impairment. Type 1 obstructions are caused by bilateral laryngeal denervation, most commonly due to bilateral recurrent laryngeal nerve palsy. Type 2 obstructions are caused by crico-arytenoid joint ankylosis. Type 3 obstructions are due to inter-arytenoid granulation tissue. Type 4 lesions are caused by mature inter-arytenoid scar tissue and type 5 lesions are complex or total laryngeal stenoses.

Purpose: Voice therapy practice and research, as in most types of rehabilitation, is currently limited by the lack of a taxonomy describing what occurs during a therapy session (with enough precision) to determine which techniques/components contribute most to treatment outcomes. To address this limitation, a classification system of voice therapy is proposed that integrates descriptions of therapeutic approaches from the clinical literature into a framework that includes relevant theoretical constructs., Method: Literature searches identified existing rehabilitation taxonomies/therapy classification schemes to frame an initial taxonomic structure. An additional literature search and review of clinical documentation provided a comprehensive list of therapy tasks. The taxonomy's structure underwent several iterations to maximize accuracy, intuitive function, and theoretical underpinnings while minimizing redundancy. The taxonomy was then used to classify established voice therapy programs., Results: The taxonomy divided voice therapy into direct and indirect interventions delivered using extrinsic and/or intrinsic methods, and Venn diagrams depicted their overlapping nature. A dictionary was developed of the taxonomy's terms, and 7 established voice therapy programs were successfully classified., Conclusion: The proposed taxonomy represents an important initial step toward a standardized voice therapy classification system expected to facilitate outcomes research and communication among clinical stakeholders.

Purpose: To compare the vocal self-perception and vocal complaints reported by two groups of students of the pedagogy course (freshmen and graduates); to relate the vocal self-perception to the vocal complaints for these groups; and to compare the voice quality of the students from these groups through perceptual auditory assessment and acoustic analysis., Methods: Initially, 89 students from the pedagogy course answered a questionnaire about self-perceived voice quality and vocal complaints. In a second phase, auditory-perceptual evaluation and acoustic analyses of 48 participants were made through voice recordings of sustained vowel emission and poem reading., Results: The most reported vocal complaints were fatigue while using the voice, sore throat, effort to speak, irritation or burning in the throat, hoarseness, tightness in the neck, and variations of voice throughout the day. There was a higher occurrence of complaints from graduates than from freshmen, with significant differences for four of the nine complaints. It was also possible to observe the relationship between vocal self-perception and complaints reported by these students. No significant differences were observed in the results of auditory-perceptual evaluation; however, some graduates had their voices evaluated with higher severity of deviation of normalcy. During acoustic analysis no difference was observed between groups., Conclusion: The increase in vocal demand by the graduates may have caused the greatest number and diversity of vocal complaints, and several of them are related to the self-assessment of voice quality. The auditory-perceptual evaluation and acoustic analysis showed no deviations in their voice.

Objectives/hypothesis: The relationship between patient-reported vocal handicap and clinician-rated measures of vocal dysfunction is not understood. This study aimed to determine if a correlation exists between the Voice Handicap Index-10 (VHI-10) and the Voice Functional Communication Measure rating in the National Outcomes Measurement System (NOMS)., Study Design: Retrospective case series., Methods: Four hundred and nine voice evaluations over 12 months at a tertiary voice center were reviewed. The VHI-10 and NOMS scores, diagnoses, and potential comorbid factors were collected and analyzed., Results: For the study population as a whole, there was a moderate negative correlation between the NOMS rating and the VHI-10 (Pearson r = -0.57). However, for a given NOMS level, there could be considerable spread in the VHI-10. In addition, as the NOMS decreased stepwise below level 4, there was a corresponding increase in the VHI-10. However, a similar trend in VHI-10 was not observed for NOMS above level 4, indicating the NOMS versus VHI-10 correlation was not linear. Among diagnostic groups, the strongest correlation was found for subjects with functional dysphonia. The NOMS versus VHI-10 correlation was not affected by gender or the coexistence of a psychiatric diagnosis., Conclusions: A simple relationship between VHI-10 and NOMS rating does not exist. Patients with mild vocal dysfunction have a less direct relationship between their NOMS ratings and the VHI-10. These findings provide insight into the interpretation of patient-perceived and clinician-rated measures of vocal function and may allow for better management of expectations and patient counseling in the treatment of voice disorders., (© 2014 The American Laryngological, Rhinological and Otological Society, Inc.)

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.

Disordered voices are frequently assessed by speech pathologists using perceptual evaluations. This might lead to problems caused by the subjective nature of the process and due to the influence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations, the design of automatic evaluation systems is desirable. With that in mind, this paper presents an automatic system which assesses the Grade and Roughness level of the speech according to the GRBAS perceptual scale. Two parameterization methods are used: one based on the classic Mel-Frequency Cepstral Coefficients, which has already been used successfully in previous works, and other derived from modulation spectra. For the latter, a new group of parameters has been proposed, named Modulation Spectra Morphological Parameters: MSC, DRB, LMR, MSH, MSW, CIL, PALA, and RALA. In methodology, PCA and LDA are employed to reduce the dimensionality of feature space, and GMM classifiers to evaluate the ability of the proposed features on distinguishing the different levels. Efficiencies of 81.6% and 84.7% are obtained for Grade and Roughness, respectively, using modulation spectra parameters, while MFCCs performed 80.5% and 77.7%. The obtained results suggest the usefulness of the proposed Modulation Spectra Morphological Parameters for automatic evaluation of Grade and Roughness in the speech.

Aim: In this study, resonance and articulation disorders were examined in a group of patients surgically treated for cleft lip and palate, considering family social background, and children's ability of self monitoring their speech output while speaking., Materials and Methods: Fifty children (32 males and 18 females) mean age 6.5 ± 1.6 years, affected by non-syndromic complete unilateral cleft of the lip and palate underwent the same surgical protocol. The speech level was evaluated using the Accordi's speech assessment protocol that focuses on intelligibility, nasality, nasal air escape, pharyngeal friction, and glottal stop. Pearson product-moment correlation analysis was used to detect significant associations between analysed parameters., Results: A total of 16% (8 children) of the sample had severe to moderate degree of nasality and nasal air escape, presence of pharyngeal friction and glottal stop, which obviously compromise speech intelligibility. Ten children (10%) showed a barely acceptable phonological outcome: nasality and nasal air escape were mild to moderate, but the intelligibility remained poor. Thirty-two children (64%) had normal speech. Statistical analysis revealed a significant correlation between the severity of nasal resonance and nasal air escape (p ≤ 0.05). No statistical significant correlation was found between the final intelligibility and the patient social background, neither between the final intelligibility nor the age of the patients., Conclusion: The differences in speech outcome could be explained with a specific, subjective, and inborn ability, different for each child, in self-monitoring their speech output.

The Singing Voice Handicap Index (SVHI) was developed in the United States for the self-assessment of patients with singing problems. It has been translated into German and its reliability and validity have been assessed. In total, 54 (35 female, 19 male) dysphonic singers and 130 (74 female, 56 male) non-dysphonic professional singers were included in the study. Reliability rested on high test-retest reliability (r = 0.960, p ≤ 0.001, Pearson correlation) and a Cronbach's α of 0.975. A principal component analysis using the Varimax method and the results of the screeplot suggest the SVHI scored as a single scale. Validity rested on a highly significant correlation between the severity of the self-rated voice impairment by the patient and the total SVHI score. Dysphonic singers have significantly higher SVHI scores than healthy singers. The SVHI is thus suited to implementation as a diagnostic tool in German-speaking countries.

Background: Occupational voice disorders are characterized by rich symptomatology, which is dominated by symptoms by the vocal tract discomfort. Therefore, in the management it is important to consider these subjective complaints. The aim of the study was to assess the usefulness of using the vocal tract discomfort scale (VTD) in evaluating the effects of rehabilitation concerning occupational dysphonia., Material and Methods: The study included 55 teachers (mean age: 47.2) with occupational dysphonia diagnosed by means of videostroboscopy. The test battery covered also: voice self-assessment according to voice handicap index (VHI), perceptual GRBAS scale and the new VTD scale. Each subject underwent intensive rehabilitation, including voice training and voice hygiene education. The post-therapy examination was conducted using the above-mentioned methods., Results: Comparing the results obtained in the preliminary and the control test a significant post-therapy improvement (p = 0.000) was discovered in relation to the general outcome of the symptoms in the VTD scale-- assessed both as for the frequency subscale (24.7 points vs. 10 points) and the severity subscale (25.5 points vs. 10 points). A positive significant effect of the treatment was also observed in the voice VHI disability index (p < 0.05) and in perceptual voice evaluation, using the GRBAS scale (p < 0.05). In addition, the study revealed a high coefficient of correlation (r) between the subscales of the VTD, both in the preliminary examination (r = 0.934) and the control one (r = 0.935)., Conclusions: The vocal tract discomfort scale (VTD) seems to be a valuable tool, useful in monitoring the progress in the treatment of occupational voice disorders.

Identification of voice disorders has a fundamental role in our life nowadays. Therefore, many of these diseases must be diagnosed at early stages of occurrence before they lead to a critical condition. Acoustic analysis can be used to identify voice disorders as a complementary technique with other traditional invasive methods, such as laryngoscopy. In this article, we followed an extensive study in the diagnosis of voice disorders using the statistical pattern recognition techniques. Finally, we proposed a combined scheme of feature reduction methods followed by pattern recognition methods to classify voice disorders. Six classifiers are used to evaluate feature vectors obtained by principal component analysis or linear discriminant analysis (LDA) as feature reduction methods. Furthermore, individual, forward, backward, and branch-and-bound methods are examined as feature selection methods. The performance of each combined scheme is evaluated in terms of the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The experimental results denote that LDA along with support vector machine (SVM) has the best performance, with a recognition rate of 94.26% and AUC of 97.94%. Additionally, this structure has the lowest complexity in comparison with other architectures. Among feature selection methods, individual feature selection followed by SVM classifier shows the best recognition rate of 91.55% and AUC of 95.80%., (Copyright © 2011 The Voice Foundation. Published by Mosby, Inc. All rights reserved.)

Background: Speech production and speech phonetic features gradually improve in children by obtaining audio feedback after cochlear implantation or using hearing aids. The aim of this study was to develop and evaluate automated classification of voice disorder in children with cochlear implantation and hearing aids., Methods: We considered 4 disorder categories in children's voice using the following definitions: Level&#95;1: Children who produce spontaneous phonation and use words spontaneously and imitatively. Level&#95;2: Children, who produce spontaneous phonation, use words spontaneously and make short sentences imitatively. Level&#95;3: Children, who produce spontaneous phonations, use words and arbitrary sentences spontaneously. Level&#95;4: Normal children without any hearing loss background. Thirty Persian children participated in the study, including six children in each level from one to three and 12 children in level four. Voice samples of five isolated Persian words "mashin", "mar", "moosh", "gav" and "mouz" were analyzed. Four levels of the voice quality were considered, the higher the level the less significant the speech disorder. "Frame-based" and "word-based" features were extracted from voice signals. The frame-based features include intensity, fundamental frequency, formants, nasality and approximate entropy and word-based features include phase space features and wavelet coefficients. For frame-based features, hidden Markov models were used as classifiers and for word-based features, neural network was used., Results: After Classifiers fusion with three methods: Majority Voting Rule, Linear Combination and Stacked fusion, the best classification rates were obtained using frame-based and word-based features with MVR rule (level 1:100%, level 2: 93.75%, level 3: 100%, level 4: 94%)., Conclusions: Result of this study may help speech pathologists follow up voice disorder recovery in children with cochlear implantation or hearing aid who are in the same age range.

Background: self-assessment of the impact of vocal deviation in the quality of life of erudite singers., Aim: to verify whether the presence of vocal complaints in erudite singers produces quality of life handicap in the use of singing voice and whether this handicap is related to gender, age, vocal classification or time of singing., Method: fifty-nine professional erudite choir singers answered the questionnaire including general questions such as identification, vocal classification, gender, time of study and dedication to classical singing. The choir singers were categorized into two groups, according to the presence of vocal complaints. They all answered the protocol Classical Singing Handicap Index (CSHI), which analyzes the impact of abnormal voice on singing voice in three subscales: Disability, Handicap and Impairment., Results: subscales Impairment (6.39) and Disability (5.39) presented higher scores than subscale Handicap (3.34) for all singers. Moreover, there was statistically significant correlation between presence of vocal complaint and higher score of CSHI (p > 0.001 to all subscales). In the group with complaints, women had higher score in subscale Disability than men. In the group without complaints, older subjects and those who had sung longer presented lower CSHI scores., Conclusion: singers with vocal complaints and/or symptoms had higher handicap index in singing, expressed in subscales Impairment and Disability, without relationship with vocal classification.

Objective: This work presents a computer-aided method for automatically and objectively classifying individuals with healthy and dysfunctional vocal fold vibration patterns as depicted in clinical high-speed (HS) videos of the larynx., Methods: By employing a specialized image segmentation and vocal fold movement visualization technique - namely phonovibrography - a novel set of numerical features is derived from laryngeal HS videos capturing the dynamic behavior and the symmetry of oscillating vocal folds. In order to assess the discriminatory power of the features, a support vector machine is applied to the preprocessed data with regard to clinically relevant diagnostic tasks. Finally, the classification performance of the learned nonlinear models is evaluated to allow for conclusions to be drawn about suitability of features and data resulting from different examination paradigms. As a reference, a second feature set is determined which corresponds to more traditional voice analysis approaches., Results: For the first time an automatic classification of healthy and pathological voices could be obtained by analyzing the vibratory patterns of vocal folds using phonovibrograms (PVGs). An average classification accuracy of approximately 81% was achieved for 2-class discrimination with PVG features. This exceeds the results obtained through traditional voice analysis features. Furthermore, a relevant influence of phonation frequency on classification accuracy was substantiated by the clinical HS data., Conclusion: The PVG feature extraction and classification approach can be assessed as being promising with regard to the diagnosis of functional voice disorders. The obtained results indicate that an objective analysis of dysfunctional vocal fold vibration can be achieved with considerably high accuracy. Moreover, the PVG classification method holds a lot of potential when it comes to the clinical assessment of voice pathologies in general, as the diagnostic support can be provided to the voice clinician in a timely and reliable manner. Due to the observed interdependency between phonation frequency and classification accuracy, in future comparative studies of HS recordings of oscillating vocal folds homogeneous frequencies should be taken into account during examination., (2010 Elsevier B.V. All rights reserved.)

Objectives: To gather information on the vocal and professional impact of supracricoid partial laryngectomy (SCPL)., Study Design: Collection of quality of life data more than 12 months postsurgery, including expert-rated patient voice assessment (GRBAS [grade, roughness, breathiness, asthenia, strain] scale), self voice assessment, Voice Handicap Index (VHI), and socioprofessional data., Patients: Sixty-four patients were evaluated after SCPL. The surgical procedure had been conducted for initial stage T1b or T2 glottic carcinoma. All patients had no recurrence, were decanulated, and did not have any swallowing impairment. Thirty-three patients were less than 60 years old and were professionally active at the time of surgery. The mean age was 48.5 years old (42-59 years old). Fifteen patients were working in the communications sector, so use of their voice was obligatory. Professional impact and VHI measurements were evaluated., Results: For patients with professional activities before surgery, 15/33 (45%) continued their activities, whereas 55% had to withdraw from or modify these activities. For patients with communications activities, 6/15 (40%) continued their activities, 4 (26.6%) adapted, and 5 (33.3%) withdrew from their professional activities. The mean VHI value was 51.2. The mean VHI values according to the dysphonic grade (1, 2, and 3) were 36.3, 50.7, and 52.9, respectively. The mean VHI values were 45.6, 58.6, and 53.2, respectively, for patients who pursued their professional activities, withdrew from or adapted their activities, and had no professional activities. Correlations were obtained between VHI and the dysphonic grade (P = .043), cessation of professional activities (P = .034), and the time elapsed since surgery (P = .046). VHI emotional and physical subscale scores were significantly higher for patients who had withdrawn from their professional activities or adapted them., Conclusions: Supracricoid partial laryngectomy can have a marked social and professional impact. Many patients have to withdraw from professional activities in which vocal involvement is essential. The potential postsurgical social voice impact should be taken into consideration before proposing this surgery, and it is essential to estimate the possible impacts of the vocal handicap according to the patient's professional or other activities.

Objective: To determine patient-perceived voice-related quality of life in patients treated with various methods based on the results of Voice-Related Quality of Life (VRQOL) and Voice Handicap Index-10 (VHI-10) questionnaires., Design: The VRQOL and VHI-10 questionnaires., Setting: University hospital., Patients: One hundred thirty-seven patients who had received definitive treatment of laryngeal cancer were followed-up at Hokkaido University Hospital, Sapporo, Japan, and were alive with no evidence of malignancy at the time of the survey., Main Outcome Measure: Patient-perceived voice-related quality of life based on the results of the VRQOL and VHI-10 questionnaires., Results: The mean VRQOL scores for patients who had undergone radiotherapy (n = 63), chemoradiotherapy (n = 29), laser surgery (n = 14), or total laryngectomy (n = 27) as final treatment of laryngeal cancer were 92.6, 92.9, 85.5, and 68.4, respectively; the mean VHI-10 scores were 2.87, 2.34, 5.43, and 11.26, respectively., Conclusion: The VRQOL and VHI-10 questionnaires are important in judging the overall effectiveness of treatment options for laryngeal cancer.

Wood and Milgrom defined vocal cord dysfunction (VCD) as paradoxical adduction of the vocal folds during inspiration or during inspiration and expiration. We describe the case of a patient with attacks of dyspnea with an isolated expiratory paradoxical adduction of the vocal folds. A review of the literature reveals many factors associated with VCD. Because of the similar risk factors and order of events concerning VCD, we believe that even expiratory laryngeal dysfunctions could be denoted as subtypes of VCD.

Introduction: In everyday phoniatrician practice there are problems in finding adequate coding in current version of International Classification of Diseases. There is grey area for additional explanation under the official diagnosis and its code in Computer program window, but that has bad consequences on efficacy in writing the final medical report., Objective: Suggestions for solving this problem that can contribute in next Revision are presented., Method: The comparation between diagnoses in 7 years period of Phoniatric Department everyday practice and given diagnoses in current version of International Classification of Diseases., Results: Certain percent of diagnoses that are overviewed during last 7 years in our Phoniatric Department has no adequate expression in given diagnoses in Tenth Revision. One can say that there is some kind of communication disorder., Conclusion: This is the moment for Communication Disorders Care Center experts to participate in constitution of widespread acceptable nomenclature. The role of phoniatrician is necessary and logical in this system. The phoniatrician aspect of contribution in next Revision, is presented.

Based on voice-history data, a chi2 test was used to investigate the difference between students of acting (n = 45) and other students (n = 45). A t-test was used to calculate the differences in acoustic parameters between the two groups. It was expected that students of acting spent significantly more time practicing voice exercises, took more acting instructions, and generally spoke more in larger rooms and did warm up exercises (p < .001). However, it was not expected that they smoked more than non-professionals (p = .003), and that they drank alcoholic drinks as much as other students. Male students of acting had significantly lower f(0) SD means (p = .015), which means that they had a more stable pitch throughout phonation. Students of acting also showed a significantly higher Harmonics-to-Noise Ratio (HNR) than other students (p = .001 for males; p = .01 for females). The data showed the importance of the appropriate use of voice, which reflected relatively good voice quality despite the bad living habits of the future professional voice users.

Variations in voice quality are essentially related to modifications of the glottal source parameters, such as: F0, jitter, and shimmer. Voice quality is affected by prosody, emotional state, and vocal pathologies. Psychogenic vocal pathology is particularly interesting. In the present case study, the speaker naturally presented a ventricular band voice whereas in a controlled production he was able to use a more normal phonation process. A small corpus was recorded which included sustained vowels and short sentences in both registers. A normal speaker was also recorded in similar tasks. Annotation and extraction of parameters were made using Praat's voice report function. Application of the Hoarseness Diagram to sustained productions situates this case in the pseudo-glottic phonation region. Analysis of several different parameters related to F0, jitter, shimmer, and harmonicity revealed that the speaker with psychogenic voice was capable of controlling certain parameters (e.g. F0 maximum) but was unable to correct others such as shimmer.

The present study deals with the dimensions of normal and pathological phonation. Separation of normal voices from pathological voices is tested under different aspects. Using a new parametrization of voice-quality properties in the acoustic signal, the vowel productions of 534 speakers (267 M, 267 F) without any reported voice pathology and the productions of 534 gender-matched pathological speakers were considered. In a first step, a gender-specific separation of the two groups is supported by a number of significantly different parameter means. In a second step, a clustering technique differentiates three subgroups within each group and gender on the basis of the acoustic parameters. Further, a statistical examination of the correct assignment in the database (DB-classification) as "normal" or "pathological" shows that the two groups overlap to some extent. The overlap of speaker assignment indicates a phonation continuum through the multidimensional space extending from normal to pathological voices. The validity of a categorical distinction of normal and pathological phonation in the sense of an individual or group-orientated labelling of voice quality as "normal" or "pathological", respectively, is discussed.

This investigation was undertaken for perceptual and acoustic analysis of voice in patients with Reinke's oedemas treated with laser microsurgery. Material of the study included 45 patients in the age of 23 to 65 presenting to Foniatric Clinic with dysphonia in Reinke's oedemas. Patients were divided into 3 groups according to Yonakawa's classification of Reinke's oedemas. All patients were treated with CO2 laser microsurgery. Voice assessment was made subiectively with GRBAS scale and objectively using multidimensional voice parameters analysis (MDVP). The examination was undertaken preoperatively and 4 weeks after operation. The results of our study show relationship between quality of voice after surgery and the stage of evolution of Reinke's oedemas according to the applied classification.

Objective: To achieve consistency and uniformity in reporting speech outcomes in individuals born with cleft palate with or without cleft lip using perceptual parameters that characterize their speech production behavior regardless of the language or languages spoken., Design: A working group of six individuals experienced in speech and cleft palate was formed to develop a system of universal parameters for reporting speech outcomes in individuals born with cleft palate. The system was adopted in conjunction with a workshop held in Washington, D.C., that was devoted to developing the universal system. The system, which was refined further following the workshop, involves a three-stage plan consisting of (1) evaluation, (2) mapping, and (3) reporting. The current report focuses primarily on the third stage, reporting speech outcomes., Results: A set of five universal speech parameters has been devised for the reporting stage. These consist of (1) hypernasality, (2) hyponasality, (3) audible nasal air emission and/or nasal turbulence, (4) consonant production errors, and (5) voice disorder. Also included are speech understandability and speech acceptability, global parameters that can be reported for any type of speech disorder. The parameters are described in detail, and guidelines for speech-sampling content and scoring procedures in relation to the parameters are presented., Conclusion: A plan has been developed to document speech outcomes in individuals with cleft palate, regardless of the spoken language, using a set of five universal reporting parameters and two global speech parameters.