Voice Signal Research Articles

Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids

<p>Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual evaluation of speech quality (PESQ), mean opinion score (MOS) for speech quality and modified rhyme test (MRT) for speech intelligibility. The test signals used for the evaluation of quality are a syllable and a word and for the evaluation of intelligibility 300 monosyllabic words are used. The results demonstrated an increase in the quality and intelligibility of processed speech in a noisy environment. As a result, there is an enhancement in perception of processed speech in a noisy environment.</p>

Linear prediction coefficients correction method for digital speech processing systems with data compression based on the autoregressive model of a voice signal

The problem of distortion of the autoregressive model of the voice signal under the influence of additive background noise in digital speech processing systems with data compression based on linear prediction is considered. In the frequency domain, these distortions are observed in the weakening of the main formants responsible for the intelligibility of the speaker’s speech. To compensate for formant attenuation, it is proposed to modify the parameters of the autoregressive model (linear prediction coefficients) using the impulse response of a recursive shaping filter. Along with the amplitude amplification of the formants, their frequencies remain unchanged to make the speaker’s voice recognizable. The effectiveness of the method was studied experimentally using specially developed software. Based on the experimental results, conclusions were drawn about a significant increase in the relative level of formants in the power spectrum of the corrected voice signal.

ESTIMATION OF FORMANT INFORMATION USING AUTOCORRELATION FUNCTION OF VOICE SIGNAL

Context. The current scientific problem of extracting biometric characteristics of a user of a voice authentication system, which can significantly increase its reliability, is considered. There has been performed estimation of formant information from the voice signal, which is a part of the user template in the voice authentication system and is widely used in the processing of speech signals in other applications, including in the presence of interfering noise components. The work is distinguished by the investigation of a polyharmonic signal. Objective. The purpose of the work is to develop procedures for generating formant information based on the results of calculating the autocorrelation function of the analyzed fragment of the voice signal and their subsequent spectral analysis. Method. The procedures for generating formant information in the process of digital processing of voice signal are proposed. Initially, the autocorrelation function of the analyzed fragment of the voice signal is calculated. Based on the results of the autocorrelation function estimation, the amplitude-frequency spectrum is calculated, from which the formant information is extracted, for example, by means of threshold processing. When the signal-to-noise ratio of the analyzed voice signal fragment is low, it is advisable to iteratively calculate the autocorrelation function. The latter allows increasing the signal-to-noise ratio and the efficiency of formant information extraction. However, each subsequent iteration of the autocorrelation function calculation is associated with an increase in the required computational resource. The latter is conditioned by the doubling of the amount of processed data at each iteration. Results. The developed procedures for generating formant information were investigated both in the processing of model and experimental voice signals. The model signals had a low signal-to-noise ratio. The proposed procedures allow to determine more precisely the width of the spectrum of extracted formant frequencies, significantly increase the number of extracted formants, including cases at low signal-to-noise ratio. Conclusions. The conducted model experiments have confirmed the performance and reliability of the proposed procedures for extracting formant information both in the processing of model and experimental voice signals. The results of the research allow to recommend their use in practice for solving problems of voice authentication, speaker differentiation, speech and gender recognition, intelligence, counterintelligence, forensics and forensic examination, medicine (diseases of the speech tract and hearing). Prospects for further research may include the creation of procedures for evaluating formant information based on phase data of the processed voice signal.

Issue Information

The voice signal distortion through various videoconferencing platforms (VCPs) and devices used in cleft palate telepractice was acoustically analyzed.FIGURE: Example of the audio waveform and amplitude spectrogram of the same voice sample in Japanese /Φ/ɯ, /ɯ/, /ts/, /ɯ/, /ɯ/ (which translates to “general“ in English) for all combinations of VCP and devices. Sub‐figures F, J, and P present the audio waveform loss in the low‐amplitude area (↓: Loss of audio waveform); and C, D, and G present the signal attenuation (→: Signal attenuation). Further details can be seen in the article by Tajiri et al. in this issue. image

A Study on the Listening Environment in Schools for the Inclusive Education of Students with Hearing Impairments

Purpose: The purpose of this study was to investigate the listening environment for students with hearing impairments in inclusive education, providing fundamental data for establishing performance standards and improving classroom listening environments for these students.Methods: The teacher’s voice signal, classroom noise, and reverberation time were measured and analyzed in general classrooms, special education classrooms, and gymnasiums from 10 elementary schools where students with hearing impairments receive inclusive education.Results: The average equivalent noise level in the general classrooms was 56.8 dBA, and it was 53.4 dBA in the special education classrooms. The signal-to-noise ratio was +12.3 dB in the general classrooms, +15.7 dB in the special education classrooms, and +5.1 dB in the gymnasiums. The reverberation time was 0.4 seconds in the general classrooms, 0.5 seconds in the special education classrooms, and 0.8 seconds in the gymnasiums.Conclusion: Currently, it can be difficult for students with hearing impairments to effectively listen in inclusive educational environments. Therefore, improvements are needed to create better listening conditions.

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium.

The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

Acoustic evaluation of voice signal distortion by videoconferencing platforms and devices used in telepractice for cleft palate.

The usefulness and effectiveness of telepractice have been reported in recent years. Treatment of cleft palate patients with compensatory articulation is based on perceptual identification. Telepractice using videoconferencing platforms causes voice signal distortion and impacts auditory-perceptual perception. This study aimed acoustically examine voice signal distortion and determine the optimal videoconferencing platforms, in addition to the phonemes that can be discriminated with the same quality as in face-to-face interactions. ATR503 with 50 phoneme-balanced Japanese speech sentences was used as a reference corpus. Four videoconferencing platforms, -Zoom, Cisco Webex, Skype, and Google Meet, -and five devices, -iPhone, Android, iPad Air, Windows, and MacBook Pro were used as transmission conditions to examine voice signal distortions with the objective measure log-spectral distortion (LSD). Tukey's test was conducted to evaluate the degree of consonant distortion related to voicings (voiceless and voiced), places of articulation (bilabial, alveolar, alveolo-palatal, palatal, velar, labial-velar, and glottal), and manners of articulation (plosive, fricative, affricate, tap or flap, nasal, and approximant). With statistically significant differences, voiced, bilabial, labial-velar, nasal, and plosive consonants exhibited smaller distortions. In contrast, voiceless, alveolo-palatal, fricative, and affricate consonants exhibited larger distortions. Google Meet exhibited the lowest distortion among videoconferencing platforms and MacBook exhibited the lowest distortion among devices. This study provides significant insights into the telepractice strategies with the appropriate videoconferencing platform and device, and useful settings for cleft palate patients with compensatory articulations with respect to acoustics.

A service failure assessment model for smart product consumption experience based on customer perception

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers’ behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Estimation of Speech Features Using a Wearable Inertial Sensor

Speech features have been investigated as novel digital biomarkers for many psychiatric and neurocognitive diseases. Microphones are the most used devices for speech recording but inevitably suffering from several disadvantages such as privacy leakage and environmental noises, limiting their clinical applications particularly for long-term ambulatory monitoring. The aim of the present study is therefore to explore the feasibility of extracting speech features from the acceleration recorded on the sternum. Ten healthy subjects volunteered in our study. Two speech tasks, i.e., repeating one sentence 20 times and reading 20 different sentences, were performed by each subject, with each task repeated 8 times under different speech rate and loudness. Voice signals and speech-caused chest vibrations were simultaneously recorded by a microphone and an accelerometer placed on the sternum. 42 acoustic features and 6 time-related prosodic features were extracted from both signals using a standard toolbox, and then compared by a linear fit and correlation analysis. Good agreement between the acceleration features and microphone features is observed in all 6 time-related prosodic features for both tasks, but only in 19 and 17 acoustic features for task 1 and 2, respectively, with most of them loudness- or pitch-related. Our results suggest the sternum acceleration to track time-related speech prosody, loudness, and pitch very well, demonstrating the feasibility of deriving digital biomarkers from the acceleration signal for diseases strongly related to time-related prosodic and loudness features.

Analysis of Voice Quality in Children With Smith-Magenis Syndrome

The production of phonation involves very complex processes, linked to the physical, clinical, and emotional state of the speaker. Thus, in populations with neurological diseases, it is possible to find the imprint in the voice signal left by the deterioration of certain cortical areas or part of the neurocognitive mechanisms that are involved in speech. In previous works, the authors determined the relationship between the pathological characteristics of the voice of the speakers with Smith-Magenis syndrome (SMS) and a lower value in the Cepstral Peak Prominence (CPP) with respect to normative speakers. They also described the presence of subharmonics in their voices. ObjectivesThe present study aims to verify whether both characteristics can be used simultaneously to differentiate SMS voices from neurotypical voices. It will also be analyzed if there is variation in the trajectory of the formants coinciding with the subharmonics. MethodsTo do this, the effect of subharmonics in the voices of 12 SMS individuals was isolated to see if they were responsible for the lower CPP values. An evaluation of the CPP was also carried out in the areas of subharmonic presence, from the peak that reflected the value of f0, rather than using the most prominent peak. This offered us a baseline for the CPP value in the presence of subharmonics. It was checked if changes in the formants occurred synchronously to the appearance of those subharmonics. If so, the muscles that control the position of the jaw and tongue would be affected at the same time as the larynx. The latter was difficult to observe since the samples were very short. A comparison of phonatory performance of a sustained /a/ between a normotypical group and non-normotypical group of children was carried out. These groups were balanced and matched in age and gender. The Spanish Association of Smith-Magenis Syndrome (ASME) provides almost 20% of the population in Spain. ResultsThe CPP allows differentiating between normative speakers and those wit SMS, even when isolating the effect of subharmonics. ConclusionsThe CPP is a robust index for determining the degree of dysphonia. It makes possible to differentiate pathological voices from healthy voices even when subharmonics are present. The presence of subharmonics is a characteristic of voices of SMS individuals and is not present in healthy ones. Both indexes can be used simultaneously to differentiate SMS voices from neurotypical voices.

Optimizing MFCC parameters for the automatic detection of respiratory diseases

Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) are widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis. In this study, we address this gap by examining the effects of key parameters, namely the number of coefficients, frame length, and hop length between frames, on respiratory condition examination. Our investigation uses four datasets: the Cambridge COVID-19 Sound database, the Coswara dataset, the Saarbrücken Voice Disorders (SVD) database, and a TACTICAS dataset. The Support Vector Machine (SVM) is employed as the classifier, given its widespread adoption and efficacy. Our findings indicate that the accuracy of MFCC decreases as hop length increases, and the optimal number of coefficients is observed to be approximately 30. The performance of MFCC varies with frame length across the datasets: for the COVID-19 datasets (Cambridge COVID-19 Sound database and Coswara dataset), performance declines with longer frame lengths, while for the SVD dataset, performance improves with increasing frame length (from 50 ms to 500 ms). Furthermore, we investigate the optimized combination of these parameters and observe substantial enhancements in accuracy. Compared to the worst combination, the SVM model achieves an accuracy of 81.1%, 80.6%, and 71.7%, with improvements of 19.6%, 16.10%, and 14.90% for the Cambridge COVID-19 Sound database, the Coswara dataset, and the SVD dataset respectively. To validate the generalization of these findings, we employ the Long Short-Term Memory (LSTM) model as a validation model. Remarkably, the LSTM model also demonstrates improved accuracy of 14.12%, 10.10%, and 6.68% across the datasets when utilizing the optimal combination of parameters. The optimal parameters are validated using an external voice pathology dataset (TACTICAS dataset). The results demonstrate the generalization capabilities of the optimized parameters across various pathologies, machine-learning models, and languages.

Artificial basilar membrane/hair cell integrated acoustic system for keyword spotting in noisy environments inspired by human cochlea

We report a novel speech recognition method using a noise-robust acoustic sensor system that integrates a spatially frequency-separating sensor with a nonlinear amplification algorithm, mimicking the cochlea’s basilar membrane and hair cells. The multichannel piezoelectric artificial basilar membrane (ABM) sensor detects specific sound frequencies with high sensitivity over 0.2–6 kHz. The signal processing model of the Artificial Hair Cell inspired by the signal transduction mechanism of inner hair cells, simultaneously enhances the frequency selectivity of ABM sensors and improves noise robustness. In a 0 dB SNR noisy environment, it effectively detected the voice signal with a maximum SNR of 57 dB. Furthermore, we converted the frequency-separated signals for speech sounds in various noisy environments into heatmap images and utilized them as input for a CNN-based speech recognition algorithm. Consequently, our system demonstrated noise-robust recognition performance with 94 % accuracy, even in noisy environments.

Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection

Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of ten common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate (ZCR), Root-Mean-Square Energy (RMSE), and Mel-frequency Cepstral Coefficients (MFCCs) using a thousand voice signals from the Saarbruecken Voice Database (SVD) dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors (KNN). The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.

Study of Multimodal Identification Algorithms using Modern Methods and Tools of Multivariate Analysis

This article aims to comprehensively investigate the theoretical and practical foundations, as well as the distinctive characteristics, underpinning the study of multimodal identification algorithms. This investigation will be conducted using state-of-the-art methods and tools of multidimensional analysis. The development of a multimodal algorithm using the method of modality fusion at the feature level encompasses the integration of various algorithms rooted in multivariate analysis. These include a combined voice activity detector, a face detector utilizing the MTCNN (multi-task cascade convolutional networks) architecture, fine-frequency cepstral coefficients, facial image features, and a decision-making module. To construct a multimodal identification algorithm, a framework for combining these algorithms based on multivariate analysis is proposed. Analysis of the acquired data indicates that “Test 1”, utilizing facial image data, exhibits the highest performance indicators, approaching nearly 100%. Tests 2 and 3 involving voice signals exhibit a minor error in the pre-processing stage, attributed to the inherent delay experienced by participants during the video conference. The proposed multimodal algorithm, integrated within a biometric identification system, enables successful user verification research through the utilization of a combined multidimensional analysis algorithm. Furthermore, the algorithm showcases superior research outcomes in comparison to other analogous multimodal identification algorithms, as it yields precise results.

An Efficient Voice-Based Emotion Recognition Using Hybrid Capsule Slime Mould Dense Deep Learning Framework

Emotion recognition is an acceptable task of understanding the other’s emotions and thoughts. Modern technology allows machines to recognize objects without the need for human intervention. The existing emotion recognition system faces more difficulties in making an accurate result with limited audio files. To address this problem, a Bag of audio terms-based hybrid deep learning models will be introduced it is known as the pioneering deep learning model. Input voice data is considered from a large dataset and pre-processed using a Data normalization and adaptive bilinear filtering approach. Afterward, acoustic features are taken out from the voice signals to capture related information for emotion recognition. These features can include linear prediction coefficients (LPC), three-dimensional (3D) log-mel spectrum, mel-frequency cepstral coefficients (MFCCs), and Prosodic features. Subsequently, feature selection is performed using an improved wild horse optimization (WHO) approach. Finally, a hybrid capsule slime mould dense deep learning framework (HCSDN) is used for voice-based emotion recognition. IEMOCAP and EMODB datasets are used to calculate system performance. The performance metrics denote the proposed system achieves 96.78% accuracy, 96.45% specificity, 95.81% precision, 4.256% error rate, and 94.256% sensitivity, 0.75% false positive rate in terms of the IEMOCAP dataset. Similarly, the proposed system achieves 96.85% accuracy, 95.74% specificity, 96.12% precision, 3.432% error rate, 95.25% sensitivity, and 0.62% false positive rate in terms of the EMODB dataset.

Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study.

Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD. We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase. A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models. The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found. Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.

Emotion Detection from EEG Signals Using Machine Deep Learning Models.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

Design of an asterisk-based VoIP system and the implementation of security solution across the VoIP network

Voice over Internet Protocol (VoIP) is a rapidly advancing technology that facilitates the transmission of voice and audio signals over the Internet or an IP-based network in real-time. This technology has seen a significant rise in demand due to its advantages over traditional circuit-switched telephony, including lower call rates, reduced operational costs, easier management, and enhanced call features. However, the growth in VoIP usage has also increased the potential for various security threats and attacks, jeopardizing the privacy, confidentiality, and integrity of transmitted data. This paper presents the design of an Asterisk-based VoIP system and the implementation of a comprehensive security solution across the VoIP network. The study involves an in-depth analysis of VoIP technology, identifying its vulnerabilities and addressing potential threats. A security framework is proposed and implemented to safeguard the VoIP network. The designed system and security solutions are rigorously tested and evaluated to ensure robustness and effectiveness. The findings highlight critical security measures necessary for protecting VoIP infrastructures and provide a framework for future research and development in securing VoIP networks.

Voice signals database of ALS patients with different dysarthria severity and healthy controls

This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F0), jitter and shimmer were calculated. The F0 standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.

DETECTION OF PARKINSON’S DISEASE DETECTION THROUGH MACHINE LEARNING

A motor complication of Parkinson’s disease (PD), which is a neurodegenerative illness, includes speech difficulties. Since the management and treatment of this disease entails clinical and pharmacological intervention, early and precise identification of PD plays a critical role in medical knowledge. In this piece, the characteristics of the speech signal and potential machinery learning methods to diagnose Parkinson’s illness are discussed. In line with standard protocols, voice signals were elicited from the participants, which comprised PD and a mere control group. From these signals, many characteristics from the spectral, temporal, and cepstral were extracted. To locate the characteristics that can provide the most informative features for the classification, feature selection methods were applied. A support vector machine (SVM) was next used to classify PD in the healthy control participants using the selected features. Key Words: Parkinson’s disease, machine learning, support vector machine, UCI machine learning repository, deep learning, confusion matrix.