Voice Pathology Detection Research Articles

Voice Pathology Detection Based on Deep Neural Network Approach

The advancement of technology offers solution to the complex problems faced by the society and brings the wellbeing of the individuals. Smart healthcare is prominent nowadays for diagnosis, treatment and constant monitoring which reduces visitation of hospital, transport cost and waiting time. Voice pathology is a decease which affects the person vocal cord so that one who facing difficult in speech. If the decease not identified in time, it leads to permanent loss of voice for an individual. Traditionally, the decease is identified through oral examination or manual procedures. Due to the advent of smart phone, one can record the voice and send it to the cloud server for processing. Our system classifies the voice data and provides the decision to the user. This greatly reduces the transport cost, and waiting time for oral examination at medical center. The mobile phone recorded the patient voice data and it will be stored into the cloud. The voice data is synthesized to signals and with the help of deep Neural network the voice pathology can be identified. The system has been tested with the data set and the test result shows promising.

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. Glottal flow waveform directly reflects the state of glottal excitation. Extracting acoustic features from glottal source signals may contribute to the detection of pathological voice. To improve the performance of voice pathology detection, this article investigates the contribution of the glottal flow waveform for pathological voice detection by evaluating the classification result using features extracted from raw speech utterances and corresponding glottal flow waveforms. The individual feature sets used are extracted from raw or glottal voice utterances with identical parameter settings, which are openSMILE acoustic features, audio features computed by Moving Picture Experts Group-7 standard and classical glottal source features. In addition, a feature selection method in terms of the wrapper approach is used to combine the single features ranked by using the Fisher discrimination ratio. Voice pathology detection experiments were carried out using Random Forest. The best accuracies of 88.52% for the Saarbrucken Voice database and 100.00% for the Massachusetts Eye and Ear Infirmary database are achieved using the combined feature set extracted from the glottal source signal, with improvement of 0.44-3.13% in the accuracies obtained by using raw speech utterances. Compared to state-of-the-art methods, the proposed method achieves the highest accuracy for the Massachusetts Eye and Ear Infirmary database and an increase of 2.75-17.16% in detection accuracy compared to other conventional pipeline systems for the Saarbrucken Voice databse. The experimental results demonstrate that using glottal flow waveform as source signal can improve the performance of pathological voice detection.

Deep Neural Network for Automatic Classification of Pathological Voice Signals

Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN. In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment. Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest). The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice.

Pathological Voice Detection from Speech Signal using SVM

Pathological Voice Detection from Speech Signal using SVM

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.

Voice Pathology Detection based on Analysis of Modulation Spectrum in Critical Bands

The paper presents an approach to the analysis of the modulation spectrum of a voice signal, in which the primary acoustic analysis is performed in bands of unequal width. Nonuniform analysis corresponds to the psychoacoustic laws of human perception of sound information. In the context of the analysis of the modulation spectrum, the considered approach can significantly reduce the resulting number of parameters, which greatly simplifies the task of detecting pathological changes in the voice signal based on the analysis of the parameters of the modulation spectrum. For frequency decomposition of a signal into bands of unequal width, two methods are considered: 1) DFT with channel combination and 2) the use of an nonuniform filter bank. The first method is characterized by a fixed time window for the analysis of all frequency components, while in the second method the time-frequency analysis plan is consistent with the critical frequency scale of the barks. For each method, a practical signal analysis circuit has been developed and described. The paper presents the experimental data on the application of the developed schemes for the analysis of the modulation spectrum to the problem of detecting pathology in a speech signal. The parameters of the modulation spectrum acted as information signs for a classifier built on the basis of linear discriminant analysis. Three different voice bases were used in the experiment (in two cases, the pathology was neurological ALS disease (amyotrophic lateral sclerosis), and in the third case, diseases of the larynx). The parameters of the modulation spectrum obtained in the DFT-based scheme with channel combining turned out to be more preferable for classification with a small number of features, however, greater accuracy (with an increase in the number of features) made it possible to obtain the parameters obtainedin the scheme based on an unequal filter bank. In all cases, the obtained classifiers were highly accurate (more than 97%). The obtained results show that the use of nonuniform time-frequency representation is preferable in the case when the analyzed signal is a sustained vowel phonation, since it provides higher accuracy of pathology detection using fewer modulation parameters

Analysis and Detection of Pathological Voice Using Glottal Source Features

Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features and investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Príncipe de Asturias (HUPA) database and the Saarbrücken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features.

Automatic Detection and Classification of Voice Pathology

The result of rough vocal use is commonly voice pathology. Poor vocal practice can result in worse exceptional of voice, vocal fatigue, and vocal stress. This research utilizes glottal signal (signal produced by vocal folds) parameters to help out in identify voice disorders linked to vocal folds pathologies. For each recorded speech, the respective glottal signal is acquired. We select the most relevant as far as pathological / normal discrimination is concerned from the enormous set of parameters obtained. In this paper a new glottal signal parameter Maximum Opening Quotient (MOQ) is calculated to find Pathological / Normal speech discrimination. Using distinct options, the outcomes are compared. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms are used for classifications. Result shows that the average efficiency rise 2.1% using the newly studied glottal parameter Maximum Opening Quotient (MOQ), which is a major contribution of this research.

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage. MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis. ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively. ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.

Meta-analysis of voice disorders databases and applied machine learning techniques.

Background and Objective: Voice disorders are pathological conditions that directly affect voice production. Computer based diagnosis may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic evaluation. The health of the Voice is assessed by several acoustic parameters. The exactness of these parameters is often linked to algorithms used to estimate them for speech noise identification. That is why main effort of the scientists is to study acoustic parameters and to apply classification methods that achieve a high precision in discrimination. The primary aim of this paper is for a meta-analysis on voice disorder databases i.e. SVD, MEEI and AVPD and machine learning techniques applied on it. Materials and Methods: This field of study was systematically reviewed in compliance with PRISMA guidelines. A search was performed with a set of formulated keywords on three databases i.e. Science Direct, PubMed, and IEEE Xplore. A proper screening and analysis of articles were performed after which several articles were also excluded. Results: Forty-five studies that fulfills the eligibility criteria were included in this meta-analysis. After applying eligibility criteria on the peer reviewed and research article and studies that were published in authentic journals and conferences proceedings till June 2020 were chosen for further full-text screening. In general, only those articles that used voice recordings from SVD, MEEI and AVPD databases as a dataset is included in this meta-analysis. Conclusion: We discussed the strengths and weaknesses of SVD, MEEI and AVPD. After detailed analysis of the studies including the techniques used and outcome measurements, it was also concluded that Support Vector Machine (SVM) is the most common used algorithm for the detection of voice disorders. Other than was also noticed that researchers focus on supervised techniques for the clinical diagnosis of voice disorder rather than using unsupervised techniques. It was also concluded that more work needs to be on voice pathology detection using AVPD database.

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.

A Survey on Signal Processing Based Pathological Voice Detection Techniques

Voice disability is a barrier to effective communication. Around 1.2% of the World's population is facing some form of voice disability. Surgical procedures namely laryngoscopy, laryngeal electromyography, and stroboscopy are used for voice disability diagnosis. Researchers and practitioners have been working to find alternatives to these surgical procedures. Voice sample based diagnosis is one of them. The major steps followed by these works are (a) to extract voice features from voice samples and (b) to discriminate pathological voices from normal voices by using a classifier algorithm. However, there is no consensus about the voice feature and the classifier algorithm that can provide the best accuracy in screening voice disability. Moreover, some of the works use multiple voice features and multiple classifiers to ensure high reliability. In this paper, we address these issues. The motivation of the work is to address the need for non-invasive signal processing techniques to detect voice disability in the general population. This paper conducts a survey related to voice disability detection methods. The paper contains two main parts. In the first part, we present background information including causes of voice disability, current procedures and practices, voice features, and classifiers. In the second part, we present a comprehensive survey work on voice disability detection algorithms. The issues and challenges related to the selection of voice feature and classifier algorithms have been addressed at the end of this paper.

Kullback–Leibler divergence and sample skewness for pathological voice quality assessment

This paper proposes new features aiming to improve the performance of an automatic voice pathology detection system. The features are designed precisely in terms of voice pathologies effects upon the speech signal. The system is intended to deliver high accuracy with a low number of parameters. Kullback–Leibler divergence (KLD) applied to consecutive frames of the speech signal provides a measure of voice instability. In this work, the KLD is applied to frame’s histogram and a modified form of its spectrum named higher amplitude suppression spectrum (HASS). The H-KLD (histogram KLD) and the HASS-KLD are two of the three features presently approached. An additional feature that provides the level of damping of the voice pitch period waveform is proposed, the short-term sample skewness of the signal. The H-KLD, the HASS-KLD, and the sample skewness are features employed along with mel-frequency cepstral coefficients (MFCC) in a voice pathology detection system. The system is composed of a Gaussian mixture models (GMM) classifier and two generalized extreme value (GEV) distribution classifiers. They are fused by means of a Gaussian naïve Bayes classifier. A standard subset of the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database is adopted for evaluating the system. The obtained global success rate of 99.55% shows that the proposed features are suitable for pathological voice quality assessment.

Electrical Modeling of Two Tube Vocal Tract for Voice Pathology Detection

Voice pathology is interrelated to a vocal folds complication, and the vocal tract area which is attached to vocal folds shows a variable shapes in case of voice pathology. This variable shapes are tested to make the division of healthy and pathological voices. So, Length and area of vocal tract are used for finding electrical parameters using transmission line theory by considering the vocal tract as a sequence of tubes. The proposed work is used voice database from the Saarbrucken Voice Database (SVD) and real-time data from the Maharishi Markandeshwar (M.M) hospital, Mullana. The proposed method achieves a higher accuracy of 91.7 percent with electrical parameters compared to 88.2 percent with the acoustic parameters and 85.3 percent with physical parameters in the detection of voice pathology.

Classification of Psychogenic and Laryngeal Voice Diseases Based on Teager Energy Operator

Among several ways of communication, the voice remains the fastest natural tool for human-to-human and human-to-machine communication. That is why the research in automatic voice pathology detection and classification area has gained much interest in the recent years. Indeed, these automatic systems may be considered as assistive tools for the physicians during the assessment stage. This latter may help them to make decision, whether the voice signal belongs to a healthy or unhealthy subject and identifies the nature of pathology. In this context, this paper provides a voice pathology detection and classification system based on wavelet analysis and Teager Energy Operator (TEO). First, we used the input voice signal that we taken from Saarbrücken Voice Database (SVD) [1], to extract a set of features. These feature vectors are fed into a Gaussian Mixture Model (GMM) [2] for the sake of classification. The obtained results are 96.66% for the detection task and 92.5 % using TEO. These results show that our proposal outperforms some state-of-art methods used in voice pathology identification.

Correction to: Towards robust voice pathology detection

Correction to: Towards robust voice pathology detection

Detection & Classification of Voice Pathology using Electrical Circuit Parameters

The classification of pathological voice is a hot topic that has been expected significant consideration. Voice pathology is related with a vocal folds difficulty, and for this reason, the vocal tract area which is joined to vocal folds demonstrate random patterns in case of a pathological voice. This random pattern is considered to distinguish healthy and pathological voices. It is possible to utilize transmission line theory in discovering automatic voice pathology detection by taking into consideration the vocal tract as acoustic lines. The work concentrates on developing a feature extraction for detecting and classifying vocal fold polyp by investigating different vocal tract parameters. In this paper, the vocal tract length and area are utilized for computing electrical parameters of the vocal tract. Furthermore, these electrical parameters are used for the classification of pathological voice. Finally, using electrical parameters 97.3% accuracy is obtained with SVM classifier when compared with 88.2% with the acoustic parameters, 85.3% accuracy considering physical parameters and other methods used in the past. The outcomes demonstrate that electrical parameters of the vocal tract can be utilized all the more successfully with better precision in voice pathology identification.

Uncertainty handling using Improvised Intuitionistic fuzzy ANN based Voice Disorder Detection

The voice pathology detection is one of the essential process which has to be determined in the earlier stages because it is a sign for raising health related problems. The aim of this paper is to handle the uncertainty in voice dataset due to inconsistency in extracting potential features and vagueness in dealing voice signals. The raw voice signals are preprocessed by feature extraction using meyer wavelet and potential features involved in voice disorder detection are done using sequential forward feature selection methods as voice preprocessing. This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing for assigning weights and thus the complete neural network performance was fine tuned instead of assigning the weights randomly. The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.

Implementation of voice pathology detection system using feature selection

Implementation of voice pathology detection system using feature selection

A Solution to the Security Authentication Problem in Smart Houses Based on Speech

Voice-operated smart houses often face security problems when there is pathology in the voice of an individual. If an error occurs during the process of authentication, then the system should detect whether this error is due to pathology or someone is trying to deceive the system. In the case of successful pathology detection, the system offers a second option of the pin code. Pathology detection is one of the major components in this research work. Voice pathology is the disorder of the vocal fold, which is normally diagnosed in people working in different areas, such as education, courtroom, etc. Many researchers have proposed different voice pathology detection systems (VPDS) where they used a common feature extraction technique, i.e., Mel Frequency cepstral coefficient (MFCC), or compared it with their proposed technique and produced results that are different in almost every case. In this paper, we propose a system that is based on MFCC features fused with pitch and it achieves 99.97% accuracy of pathology detection for the MEEI data set. This voice pathology detection accuracy is better as compared to most of the VPDS available for MEEI.