Pathological Voices Research Articles

Parkinson’s disease and cleft lip and palate of pathological speech diagnosis using deep convolutional neural networks evolved by IPWOA

A high-level abstract idea of speech is created in previously autonomous systems using unsupervised models. Given that these representations are typically acquired by input reconstruction, it cannot be said with assurance that they are resistant to cues unrelated to disease. Pathology diagnosis cannot usually be reliably performed using unsupervised representations. As a result, in this research, we use pathological voice recognition using deep convolutional neural networks (DCNNs). Even though DCNNs have many acknowledged benefits, selecting the best structure for them can be challenging. This work examines the use of the whale optimization algorithm (WOA) to automatically choose the best architecture for DCNNs in an effort to address this constraint. In order to achieve the goal, three canonical WOA-based innovations are suggested. First, a special encoding technique based on Internet Protocol Addresses (IPA) is created to make it easier to encode the DCNN layers with whale vectors. The development of variable-length DCNNs is then suggested using an enfeebled layer that has particular whale vector dimensions. The final step in the learning process involves splitting huge datasets into smaller ones and then randomly reviewing them. Pathological audio signals captured from patients are used to assess the performance of the proposed model. In this regard, five measures were used to conduct thorough research, including ROC and precision-recall curves, F1-Score, sensitivity, specificity, accuracy, and precision. Up to 95.77 percent of the two disordered speech signals are correctly classified by the suggested model, which outperforms the second-best algorithm, VLNSGA-II, by 1.02 percent in terms of accuracy.

Harmonics-to-noise ratio estimation with deterministically time-varying harmonic model for pathological voice signals.

The harmonics-to-noise ratio (HNR) and other spectral noise parameters are important in clinical objective voice assessment as they could indicate the presence of nonharmonic phenomena, which are tied to the perception of hoarseness or breathiness. Existing HNR estimators are built on the voice signals to be nearly periodic (fixed over a short period), although voice pathology could induce involuntary slow modulation to void this assumption. This paper proposes the use of a deterministically time-varying harmonic model to improve the HNR measurements. To estimate the time-varying model, a two-stage iterative least squares algorithm is proposed to reduce model overfitting. The efficacy of the proposed HNR estimator is demonstrated with synthetic signals, simulated tremor signals, and recorded acoustic signals. Results indicate that the proposed algorithm can produce consistent HNR measures as the extent and rate of tremor are varied.

Acoustic Voice Measures in Benign Mass Lesions

Objectives: The present study aims to compare acoustic voice parameters in patients with vocal cord nodules, polyps, and normal subjects. Methods: In this cross-sectional case-control study, the participants were selected by convenience sampling, including 30 patients with vocal polyps for the first group, 38 patients with vocal nodules for the second group, and 42 participants without voice pathologies as the control group. For each group, participants were examined by a speech and language pathologist (SLP) using an Endo-vision laryngoscope. Acoustic voice parameters were measured by MDVP software in Visi-Pitch; then, these parameters were compared in groups in terms of sex. Results: Three groups showed significant differences in terms of fundamental frequency (P<0.001), jitter (P<0.001), shimmer (P<0.001), and noise-to-harmonic ratio (NHR) values (P<0.001). Women in three groups had the substantial differences in terms of fundamental frequency (P<0.001), jitter (P<0.001), shimmer (P<0.001), and NHR (P<0.001). For men, the three groups showed a significant difference for jitter (P<0.001) and shimmer (P=0.001), but for NHR (P=024), and fundamental frequency (P=0.93) no signification difference were observed. Discussion: It seems that the existence of benign mass lesions, such as vocal cord nodules and polyps affects the acoustic parameters of the voice. The measurement of acoustic voice parameters may help us to differentiate between the voices of normal people and patients with vocal cord nodules and polyps as a supplementary assessment in clinical procedures.

Pathological voice detection based on gammatone short time spectral self-similarity

The acoustic detection method based on machine learning and signal processing is an important method of pathological voice detection and the extraction of voice features is one of the most important. Currently, the features widely used have disadvantage of dependence on the fundamental frequency extraction, being easily affected by noise and high computational complexity. In view of these shortcomings, a new method of pathological voice detection based on multi-band analysis and chaotic analysis is proposed. The gammatone filter bank was used to simulate the human ear auditory characteristics to analyze different frequency bands and obtain the signals in different frequency bands. According to the characteristics that turbulence noise caused by chaos in voice will worsen the spectrum convergence, we applied short time Fourier transform to each frequency band of the voice signal, then the feature gammatone short time spectral self-similarity (GSTS) was extracted, and the chaos degree of each band signal was analyzed to distinguish normal and pathological voice. The experimental results showed that combined with traditional machine learning methods, GSTS reached the accuracy of 99.50% in the pathological voice database of Massachusetts Eye and Ear Infirmary (MEEI) and had an improvement of 3.46% compared with the best existing features. Also, the time of the extraction of GSTS was far less than that of traditional nonlinear features. These results show that GSTS has higher extraction efficiency and better recognition effect than the existing features.

Pathological Voice Detection Using Joint Subsapce Transfer Learning

A pathological voice detection system is designed to detect pathological characteristics of vocal cords from speech. Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target domains, respectively. Due to the difference in speech disease etiology, recording environment, and device, etc., the feature distributions of source and target domain are quite different. Meanwhile, considering the high costs of annotating labels, it is hard to acquire labeled data in the target domain. This paper attempts to formulate cross-domain pathological voice detection as an unsupervised domain adaptation problem. Joint subspace transfer learning (JSTL) aims to find a projection matrix to transform source and target domain data into a common space. The maximum mean discrepancy function is used to measure the divergence across databases. Intra-class and inter-class distance act as regularization to guarantee the maximum separability between different classes. A graph matrix is constructed to help transfer knowledge from the relevant source data to the target data. Three popular pathological voice databases were selected in this paper. For six cross-database experiments, the accuracy of the method proposed increased by up to 15%. For different voice categories, the category of structural voice showed the most significant increase, nearly 20%.

Calidad de la voz cantada en sopranos. Influencia del texto y el acompañamiento musical en el canto de ópera

Introduction and objectivesOpera singing voice uses different technical tools for artistic purposes. We want to define if the quality of the sung sound is affected by an aware work on the musical accompaniment and the text. We analyze the acoustic signal and the subjective perception. The pitch studied in the soprano voice is A4 (880Hz) with the vowel /a/. We have chosen a tone and a vowel that can be generated by different strategies in terms of phonoresonance adjustments. Materials and methodsWe have carried out a prospective study with 20 sopranos, without voice pathology, who have sung a phrase from the aria Deh, vieni non tarda and another from the aria Dove sono i bei momenti from the opera Le nozze di Figaro, by W.A. Mozart. First, each phrase sung spontaneously was recorded and then a second time, after a proposed work on the content of the text and musical aspects of the accompaniment such as: rhythm, harmony, texture and direction of the phrase. The participants prolonged the emission of the A4 for more than three seconds, maintaining the context of the sentence. The acoustic signal has been analyzed using the PRAAT program and a VAS (Visual Analogue Scale) questionnaire has been used to collect subjective perceptions. ResultsThe mean age was 36±11 years (range between 20 and 58) and the mean number of years singing was 17±12 years (range between 3 and 35). No statistically significant differences were found, although an improvement in the VAS was seen in the second sentence after the intervention. ConclusionsThe acoustic analysis parameters are stable, and the VAS tends to improve when an awareness of the text and the instrumental accompaniment is carried out.

An iOS-based VoiceScreen application: feasibility for use in clinical settings-a pilot study.

To elaborate the application suitable for smartphones for estimation of Acoustic Voice Quality Index (AVQI) and evaluate its usability in the clinical setting. An elaborated AVQI automatization and background noise monitoring functions were implemented into a mobile "VoiceScreen" application running the iOS operating system. A study group consisted of 103 adult individuals with normal voices (n = 30) and 73 patients with pathological voices. Voice recordings were performed in the clinical setting with "VoiceScreen" app using iPhone 8 microphones. Voices of 30 patients were recorded before and 1month after phonosurgical intervention. To evaluate the diagnostic accuracy differentiating normal and pathological voice, the receiver-operating characteristic statistics, i.e., area under the curve (AUC), sensitivity and specificity, and correct classification rate (CCR) were used. A high level of precision of AVQI in discriminating between normal and dysphonic voices was yielded with corresponding AUC = 0.937. The AVQI cutoff score of 3.4 demonstrated a sensitivity of 86.3% and specificity of 95.6% with a CCR of 89.2%. The preoperative mean value of the AVQI [6.01(SD 2.39)] in the post-phonosurgical follow-up group decreased to 2.00 (SD 1.08). No statistically significant differences (p = 0.216) between AVQI measurements in a normal voice and 1-month follow-up after phonosurgery groups were revealed. The "VoiceScreen" app represents an accurate and robust tool for voice quality measurement and demonstrates the potential to be used in clinical settings as a sensitive measure of voice changes across phonosurgical treatment outcomes.

Hypersensitivity to passive voice hearing in hallucination proneness.

Voices are a complex and rich acoustic signal processed in an extensive cortical brain network. Specialized regions within this network support voice perception and production and may be differentially affected in pathological voice processing. For example, the experience of hallucinating voices has been linked to hyperactivity in temporal and extra-temporal voice areas, possibly extending into regions associated with vocalization. Predominant self-monitoring hypotheses ascribe a primary role of voice production regions to auditory verbal hallucinations (AVH). Alternative postulations view a generalized perceptual salience bias as causal to AVH. These theories are not mutually exclusive as both ascribe the emergence and phenomenology of AVH to unbalanced top-down and bottom-up signal processing. The focus of the current study was to investigate the neurocognitive mechanisms underlying predisposition brain states for emergent hallucinations, detached from the effects of inner speech. Using the temporal voice area (TVA) localizer task, we explored putative hypersalient responses to passively presented sounds in relation to hallucination proneness (HP). Furthermore, to avoid confounds commonly found in in clinical samples, we employed the Launay-Slade Hallucination Scale (LSHS) for the quantification of HP levels in healthy people across an experiential continuum spanning the general population. We report increased activation in the right posterior superior temporal gyrus (pSTG) during the perception of voice features that positively correlates with increased HP scores. In line with prior results, we propose that this right-lateralized pSTG activation might indicate early hypersensitivity to acoustic features coding speaker identity that extends beyond own voice production to perception in healthy participants prone to experience AVH.

A hybrid model for pathological voice recognition of post-stroke dysarthria by using 1DCNN and double-LSTM networks

Post-stroke dysarthria (PSD) is a common and persistent sequela of stroke. To assist objective assessment of dysarthria, the pathological voice recognition technology has emerged. But most existing methods lacks pathological voices to be analyzed and has not high recognition rate. To solve above problems, this paper proposes a hybrid recognition model combining 1DCNN and Double-LSTM (DLSTM) networks based on the MFCC features of pathological voices. First, a syllable pronunciations’ database collected from Mandarin-speaking participants including normal adults (NA) and patients with PSD is constructed. Then, a 1DCNN network is applied to process the MFCC features of syllable pronunciations and extract the deeper hidden features. Then, by using the above implied features, a recognition model based on DLSTM network is constructed to realize the syllable-level classification. Finally, the aggregation technique is combined to determine whether the speaker belongs to the category of PSD in speaker-level. The experimental results show that the further processing of MFCC features by 1DCNN can enhance significantly the performance of the DLSTM-based recognition model with the accuracy of 82.1% and 97.4% in syllable-level and speaker-level. In the case of deep learning networks, the performance of MFCC-based models is better than those spectrogram-based models; and the combination of 1DCNN and DLSTM cannot enhance the performance of spectrograms-based models. Thus, our method can improve the efficiency of the treatment of speech disorders and effectively assist the diagnosis of PSD.

A review on voice pathology: Taxonomy, diagnosis, medical procedures and detection techniques, open challenges, limitations, and recommendations for future directions

Abstract Speech is a primary means of human communication and one of the most basic features of human conduct. Voice is an important part of its subsystems. A speech disorder is a condition that affects the ability of a person to speak normally, which occasionally results in voice impairment with psychological and emotional consequences. Early detection of voice problems is a crucial factor. Computer-based procedures are less costly and easier to administer for such purposes than traditional methods. This study highlights the following issues: recent studies, methods of voice pathology detection, machine learning and deep learning (DL) methods used in data classification, main datasets utilized, and the role of Internet of things (IoT) systems employed in voice pathology diagnosis. Moreover, this study presents different applications, open challenges, and recommendations for future directions of IoT systems and artificial intelligence (AI) approaches in the voice pathology diagnosis. Finally, this study highlights some limitations of voice pathology datasets in comparison with the role of IoT in the healthcare sector, which shows the urgent need to provide efficient approaches and easy and ideal medical diagnostic procedures and treatments of disease identification for doctors and patients. This review covered voice pathology taxonomy, detection techniques, open challenges, limitations, and recommendations for future directions to provide a clear background for doctors and patients. Standard databases, including the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and the Arabic Voice Pathology Database, were used in most articles reviewed in this article. The classes, features, and main purpose for voice pathology identification are also highlighted. This study focuses on the extraction of voice pathology features, especially speech analysis, extends feature vectors comprising static and dynamic features, and converts these extended feature vectors into solid vectors before passing them to the recognizer.

Videokymogram Analyzer Tool: Human–computer comparison

Videokymography (VKG) is a modern video recording technique used in laryngology and phoniatrics to examine vocal fold vibrations. To obtain quantitative information on the vocal fold vibration, VKG image analysis is needed but no software has yet been validated for this purpose. Here, we introduce a validated software tool that aids clinicians to evaluate diagnostically important vibration characteristics in VKG and other types of kymographic recordings. State-of-the-art methods for automated image evaluation were implemented and tested on a set of videokymograms with a wide range of vibratory characteristics, including healthy and pathologic voices. The automated image segmentation results were compared to manual segmentation results of six evaluators revealing average differences smaller than one pixel. Furthermore, the automatically categorized vibratory parameters precisely agreed with the average visual assessment in 84 and 91 percent of the cases for pathological and healthy patients, respectively. Based on these results, the newly developed software was found to be a valid, reliable automated tool for the quantification of vocal fold vibrations from VKG images, offering a number of novel features relevant for clinical practice.

Automatic Speech Recognition on Non-Pathological Dataset of Urdu Language

One of its subsystems, speech, has a strong underlying characteristic and a distinct voice. Voice disorders are abnormal conditions that influence the quality of voice. Several protocols, including acoustic analysis, can detect clinical voice pathology. Based on a computerized acoustic analysis, machine learning algorithms and non-invasive systems may play a very vital part in initial detection, tracking, and even growth of proficient pathological speech analysis. The aim of this research paper is to collect a non-pathological dataset i.e. healthy voice dataset. Two important and critical features; 1) MFCC and 2) Pitch are used to generate a final audio clip. SVM used as a classifier to train and test the dataset model and the models exhibited reasonably high training and testing accuracies i.e. 85.886% which proves to be a milestone on Urdu language dataset.

Detection of speech pathology based on parameters of analysis of dysphonia in speech and voice

Objective:To analysis speech pathology based on dysphonia in speech and voice（ADSV）. Methods:The acoustic signals of continuous vowels and continuous speech of one-hundred and thirteen individuals were collected, including 93 vocal cord polyps cases, 20 glottis laryngeal carcinoma cases and 47 volunteers without speech sound disorders. Cepstral peak prominence（CPP）, CPP standard deviation（CPP SD）, L/H spectral ratio（L/H ratio）, L/H ratio standard deviation（L/H ratio SD） and cepstral/spectral index of dysphonia（CSID） were analyzed by ADSV to explore the role of these parameters in the recognition of speech pathology. Results:In the acoustic signal of continuous vowels, CPP and L/H ratio in normal group were higher than those in pathological voice group（P<0.001）, while CPP SD and CSID were lower than those in pathological voice group（P<0.001）, CPP and CSID areas under ROC curve were 0.95 and 0.99, respectively, which were important acoustic parameters for diagnosing pathological voice. In continuous speech acoustic signals, CPP, CPP SD and L/H ratio in the normal group were all higher than those in the speech disorders group（P<0.001）, and the area under the curve of CPP SD was 0.90, which showed high accuracy in diagnosing pathological voice. The ADSV voice analysis parameters CPP, CPP SD, CSID, and L/H ratio also showed significant differences between the vocal cord polyp group and the glottic laryngeal cancer group. The results of the discriminant analysis model show that the use of ADSV voice parameters can distinguish vocal cord polyps and laryngeal cancers. Conclusion:The ADSV voice analysis parameters can not only distinguish the voice signals of the normal group and the pathological group, but also distinguish different types of pathological voices. It has high sensitivity and specificity in diagnosing pathological voices.

Classification of pathological disorders using optimization enabled deep neuro fuzzy network

The discovery of voice pathology is an emerging domain in biomedical engineering. The assessment helps to offer information for effective diagnosis. An optimization-enabled deep model for pathological disorder classification is devised in this paper. The speech signal is employed as the input of the pre-processor which subsequently helps to eliminate noise considering the Hanning window. The initial improvement is done by exploiting multi-band spectral subtraction for generating frames. The enhancement of speech is performed using Deep Residual Network (DRN) classifier. The training of DRN is performed by exploiting adopted Feedback Artificial Tree Firefly algorithm (FATFA) that is attained by combining Feedback Artificial Tree Algorithm (FAT), and Firefly algorithm (FA). The features, like Amplitude modulation spectrogram (AMS), and frequency-domain features namely tonal power ratio, pitch chroma, spectral spread, spectral flux, as well as spectral autocorrelation is mined from the improved speech. The classification is done with Deep Neuro-Fuzzy Network (DNFN), which is tuned with the devised Verse FATFA. The proposed Verse FATFA is conceived by merging FATFA and Multiverse Optimizer (MVO). Therefore, the devised Verse FATFA-based DNFN categories voice disorders into Laryngitis, Dysphonie, spasmodischedysphonie, Leukoplakie, Cyste, Dish-Syndrom, and Monochorditis. The proposed Verse FATFA-based DNFN provides enhanced performance with maximum values of 94.93% accuracy, 95.94% sensitivity with 91.93% specificity.

Dysphonia in Smokers of Combustible Cigarettes and E-cigarettes Measured Using the Filipino Voice Handicap Index

ABSTRACT Objectives: To determine the prevalence of dysphonia, defined as any perceived voice pathology, in conventional cigarette smokers and e-cigarette users and to quantify and compare the Filipino Voice Handicap Index (VHI) scores of the two groups based on the mean scores for each of the three domains of this tool, as well as the mean total score for each group. Methods: Design: Cross-sectional study Setting: Tertiary National University Hospital Participants: 52 adults between the ages 18-65 with no previously known laryngeal illness or condition were divided into 26 conventional smokers and 26 e-cigarette users and completed the self-administered Filipino Voice Handicap Index. Results: The prevalence of impairment in the sample using a total VHI score cut-off of 18 was 17.31% (9 out of 52, CI 8.23-30.32%) and the prevalence of dysphonic symptoms in the sample was 86.54% (45 out of 52, CI 74.21-94.41%). There were no significant differences between smokers and e-cigarette users for impairment using this cut-off (z: -1.36, p: .07) and dysphonic symptoms (z: 0.4063, p: .68). The prevalence of moderate impairment was 3.85% (1 out of 26, CI: 0.10-19.64%) among those using e-cigarettes; and 1.92% (1 out of 52, CI: 0.04-10.26%) among the entire sample population. Conclusion: There appears to be no statistically significant difference between the Filipino VHI scores of conventional smokers and e-cigarette users. Further inquiry into the subject would benefit from a larger sample size, comparison with a control group, inclusion of other factors relevant to the development of dysphonia, and correlation with objective means for voice analysis.

Влияние условий труда на качество жизни и степень дисфонии у педагогов. гигиенические рекомендации по их профилактике

The purpose of our research was to provide a general hygienic assessment of an effect produced by labor conditions on quality of life dependent on the prevalence rate of parameters that form the voice pathology, and to develop prevention recommendations. The interrelation between objective complaints and diagnosed voice pathologies was examined. 224 teachers aged ≥ 38 y. o. who participated in the study were selected based on subjective complaints and objective data. Two examined groups of teachers were formed during the preliminary survey. The first control group consisted of 30 healthy non-smoking men and 70 women. The second group included 26 men and 98 women with different voice disorders. 79% of those surveyed had voice disorders (dysphonia), and 28% had aphonia, which occurred in those surveyed 2–5 times during the academic year. Over 70% of teachers from the second group lost their voice strength, 61% complained of periodically occurring hoarse and husky voice, one third (32%) had dry cough, every fourth person (24%) complained of decreased voice pitch. The extraordinary majority of teachers (77%) associated their voice distortion with a great professional vocal load. Remote results obtained at six months after the therapeutic and preventive course of Homeovox in teachers of higher educational institutions with a voice pathology significantly confirmed life quality improvement.

Application of Speech Processing for Pathological Voice Detection and Analysis

Application of Speech Processing for Pathological Voice Detection and Analysis

Computer Modeling of Human Voice System Diagnostic Algorithms

Every year, people are faced with an increasing number of diseases that require timely detection and diagnosis without causing discomfort to the patient. Thus arose the systems of objective and subjective assessment of the quality of hearing, as well as the first systems of analysis of the state of the vocal tract. The problem of voice pathology is inherent in a fairly large risk group, which includes teachers, artists, call center operators. The specifics of the development of such systems is that the hardware and software complex developed by the engineer should be convenient for use by a doctor and relevant in terms of existing solutions to identify certain acoustic parameters of the voice. The study of the voice spectrum can be the main diagnostic test in the diagnosis of persistent voice disorders and, along with the study of the voice field and vibrometry, allows you to determine the form of voice disorders in professionals. Acoustic tests for the presence of high and low singing forms in the singing voice can be highly important in determining the singer's performance and professional prognosis, can serve as a criterion in the diagnosis of persistent voice disorders, and their use in early stages of occupational laryngeal diseases will help prevent preventive measures. MATLAB software comes with a large number of tools, which facilitates the implementation of many engineering, mathematical, computational issues of development and research of various processes related to any field of research. Based on these tools, there are a large number of basic functions of digital signal analysis, including audio signals. FFT - fast Fourier transform algorithms are chosen as a basis, which with a certain modification are divided into parametric and nonparametric. In this case, the nonparametric Welch method and the parametric Berg method are chosen. The user is given access to choose between the parameters required for their operation. For the first it is the dimension of the weight function, for the second it is the order of the autoregressive model. All this gives opportunity to analyze the spectrum of vowel phonemes. The AppDesigner package provides great opportunities for creating interfaces in software development. By manipulating the functions of Callback, you can bring the program to the finest settings, which at first may seem invisible, but generally create comfort when working. It is important to build a certain algorithm of action of each component. It often happens that it is necessary to take into account such details for which a certain component in itself is not responsible. For example, this could be changing the signatures of other components. To simplify code writing, it is important to create m-functions. However, this must be taken into account when editing them after entering them in the main script. Using the above toolbox, a software interface was developed, which is divided into two working areas: time and spectral parts. In addition, the interface is filled with controls for input data and spectrum analyzer parameters, as well as spectrum analysis tools.

Efficacy of Cepstral Measures in Voice Disorder Diagnosis: A Literature Review

Introduction: The acoustic analysis is one of the well-known methods for voice evaluation. In recent years, many studies have investigated the cepstral measures compared with the other former acoustic parameters. This review article evaluates the related studies in the cepstral areas to ascertain whether they are efficient in the diagnosis of dysphonia.Materials and Methods: We reviewed the available research studies between 2009 and 2021 narratively in PubMed, Scopus, Google Scholar, and Science Direct databases. The searched keywords included “cepstral peak prominence”, “smoothed cepstral peak prominence”, “instrumental acoustic analysis”, “acoustic”, and “diagnosis”. The articles that investigated the power of Cepstral Peak Prominence (CPP) and its smoothed version (CPPS) to differentiate dysphonia versus normal voice have been included. However, the interventional studies that consider CPP and CPPS as one of their adjunct variables and studies that investigated the relationship of the cepstral measure with other parameters were not included.Results: Recent studies support the efficiency of CPP and CPPS to diagnose dysphonia.Conclusion: It is reasonable for the voice care teams to use CPP and CPPS in the patients’ initial assessment and track the effects of treatment. However, according to the relatively limited number of studies in this area, more studies are required to clarify the efficacy of cepstral measures in different voice pathologies.

Neurogenerative Disease Diagnosis in Cepstral Domain Using MFCC with Deep Learning.

Because underlying cognitive and neuromuscular activities regulate speech signals, biomarkers in the human voice can provide insight into neurological illnesses. Multiple motor and nonmotor aspects of neurologic voice disorders arise from an underlying neurologic condition such as Parkinson's disease, multiple sclerosis, myasthenia gravis, or ALS. Voice problems can be caused by disorders that affect the corticospinal system, cerebellum, basal ganglia, and upper or lower motoneurons. According to a new study, voice pathology detection technologies can successfully aid in the assessment of voice irregularities and enable the early diagnosis of voice pathology. In this paper, we offer two deep-learning-based computational models, 1-dimensional convolutional neural network (1D CNN) and 2-dimensional convolutional neural network (2D CNN), that simultaneously detect voice pathologies caused by neurological illnesses or other causes. From the German corpus Saarbruecken Voice Database (SVD), we used voice recordings of sustained vowel /a/ generated at normal pitch. The collected voice signals are padded and segmented to maintain homogeneity and increase the number of samples. Convolutional layers are applied to raw data, and MFCC features are extracted in this project. Although the 1D CNN had the maximum accuracy of 93.11% on test data, model training produced overfitting and 2D CNN, which generalized the data better and had lower train and validation loss despite having an accuracy of 84.17% on test data. Also, 2D CNN outperforms state-of-the-art studies in the field, implying that a model trained on handcrafted features is better for speech processing than a model that extracts features directly.