Oropharyngeal 24-Hour pH Monitoring in Children With Airway-Related Problems

Objectives Diagnosis and clinical presentation of pediatric laryngopharyngeal reflux (LPR) is still controversial. The aims of this work were to study the possibility of performing 24-hour oropharyngeal pH monitoring for children in the outpatient clinic setup and to explore the results of this test in correlation to airway-related problems. Methods In this descriptive qualitative study, 26 children suffering from airway-related problems were included. Oropharyngeal 24-hour pH monitoring was performed for all subjects in the outpatient clinic setting. The distribution of airway diagnoses among the study group was studied versus the results of the pH monitoring. Results There were 16 males and 10 females participated in the study with a mean age of 6.88 (SD, ±5.77) years. Thirty-five percent of the patients were under the age of 3 years (range, 11 months to 3 years). Eight-five percent of the patients tolerated the pH probe insertion and completed 24-hour of pH recording. Laryngomalacia and subglottic stenosis (SGS) were more frequently reported in the positive LPR patients (77%). Conclusion Oropharyngeal 24-hour pH monitoring can be conducted for children in the outpatient setup even in young age children below 3 years old. Among the positive LPR group, SGS and laryngomalacia were the most commonly reported airway findings

Formant analysis in dysphonic patients and automatic Arabic digit speech recognition

<p>Abstract</p> <p>Background and objective</p> <p>There has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice.</p> <p>Materials and methods</p> <p>The speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal.</p> <p>Results</p> <p>There was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment.</p> <p>Conclusion</p> <p>The results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients.</p

Medialization Thyroplasty Using Autologous Nasal Septal Cartilage for Treating Unilateral Vocal Fold Paralysis

ObjectivesA persistent insufficiency of glottal closure is mostly a consequence of impaired unilateral vocal fold movement. Functional surgical treatment is required because of the consequential voice, breathing and swallowing impairments. The goal of the study was to determine the functional voice outcomes after medialization thyroplasty with using autologous septal cartilage from the nose.MethodsExternal vocal fold medialization using autologous nasal septal cartilage was performed on 15 patients (6 females and 9 males; age range, 30 to 57 years). Detailed functional examinations were performed for all the patients before and after the surgery and this included perceptual voice assessment, laryngostroboscopic examination and acoustic voice analysis.ResultsAll the patients reported improvement of voice quality post-operatively. Laryngostroboscopy revealed almost complete glottal closure after surgery in the majority of patients. Acoustic and perceptual voice assessment showed significant improvement post-operatively.ConclusionMedialization thyroplasty using an autologous nasal septal cartilage implant offers good tissue tolerability and significant improvement of the subjective and objective functional voice outcomes

Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms

A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database

An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification

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

Computer-based Blind Diagnostic System for Classification of Healthy and Disordered Voices

A large population around the world is suffering from voice-related complications. Computer-based voice disorder detection systems can play a substantial role in the early detection of voice disorders by providing complementary information to early-career otolaryngologists and general practitioners. However, various studies have concluded that the recording environment of voice samples affects disorder detection. This influence of the recording environment is a major obstacle in developing such systems when a local voice disorder database is not available. In addition, sometimes the number of samples is not sufficient for training the system. To overcome these issues, a blind detection system for voice disorders is designed and implemented in this study. Hence, without any prior knowledge of voice disorders, the proposed system has the ability to detect those disorders. The developed system relies only on healthy voice samples which can be recorded locally in the desired environment. The generation of a reference model for healthy subjects and decision criteria to detect voice disorders are two major tasks in the proposed systems. These tasks are implemented with two different types of speech features. Moreover, the unsupervised reference model is created by using DBSCAN and k-means algorithms. The overall performance of the system is 74.9 % in terms of the geometric mean of sensitivity and specificity. The results of the proposed system are encouraging and better than the performance of Multidimensional Voice Program (MDVP) parameters which are widely used for disorder assessment by otolaryngologists in clinics

Intra- and Inter-database Study for Arabic, English, and German Databases:Do Conventional Speech Features Detect Voice Pathology?

A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection