Beyond the immediate content of speech, the voice can provide rich information about a speaker's demographics, including age and gender. Estimating a speaker's age and gender offers a wide range of applications, spanning from voice forensic analysis to personalized advertising, healthcare monitoring, and human-computer interaction. However, pinpointing precise age remains intricate due to age ambiguity. Specifically, utterances from individuals at adjacent ages are frequently indistinguishable. Addressing this, we propose a novel, end-to-end approach that deploys Mozilla's Common Voice dataset to transform raw audio into high-quality feature representations using Wav2Vec2.0 embeddings. These are then channeled into our self-attention-based convolutional neural network (CNN) model. To address age ambiguity, we evaluate the effects of different loss functions such as focal loss and Kullback-Leibler (KL) divergence loss. Additionally, we evaluate the accuracy of the estimation at different durations of speech. Experimental results from the Common Voice dataset underscore the efficacy of our approach, showcasing an accuracy of 87% for male speakers, 91% for female speakers and 89% overall accuracy, and an accuracy of 99.1% for gender prediction.
The serum protein test includes measurement of the level of total protein(albumin, globulin). Fetuin-A is a blood protein made in liver. It can inhibit insulin receptor, enhance insulin sensitivity and make the individuals more likely to develop type 2 diabetes, then disorder in lipid profile (Total cholesterol(TC), low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c), Triglyceride(TG) and very low density lipoprotein cholesterol (VLDL-c) . To evaluate Fetuin-A, total protein, albumin, globulin, HbAlc and lipid profile in 200 adult and elderly Iraqi patients with type 2 Diabetes Mellitus were taken and compare them with 200 subjects as a healthy control. The laboratory analysis(for patients and
... Show MoreBackground: Thyroid cancer (TC) is an increasingly prevalent malignancy throughout the world. It has long been recognized that the incidence of TC is higher in women which increases with age. However, the association of gender disparity and age with TC aggressiveness and outcomes are still controversial. The aim of this study was focused on the association of age and gender with histopathological characteristics in TC. Methods: 153 patients who met the criteria, were selected. The included cases were divided into four age groups (≤24 years, 25-44 years, 45-64 years, and ≥65 years). Demographic, age and pathological parameters were compared among them. The association of gender and age with
... Show MoreBackground : Although development and progress in various diagnostic methods, but still identification of remnants of skeletal and decomposing parts of human is one of the most difficult skills in forensic medicine . Gender and age estimation is also considering an important problem in the identification of unknown skull. The aims of study: To estimate volume and dimension of maxillary sinus in individuals with dentate and edentulous maxillae using CT scan, and to correlate the maxillary sinus volume in relation to gender and age. Materials and Methods : This study included 120 patients ranged from (40-69 years), divided into two groups, dentate group with fully dentate maxilla and edentulous group with complete edentulous maxilla, and e
... Show MoreA case-control study was performed to examine age, gender, and ABO blood groups in 1014 Iraqi hospitalized cases with Coronavirus disease 2019 (COVID-19) and 901 blood donors (control group). The infection was molecularly diagnosed by detecting coronavirus RNA in nasal swabs of patients.
Mean age was significantly elevated in cases compared to controls (48.2 ± 13.8
ABSTRACT Background: Dental caries is a most common social and intractable infectious disease in human. Saliva is critical for preserving and maintaining oral health and salivary elements had many effects on caries experience. Aim of study: This study was conducted to assess dental caries severity by age and gender and their relation to salivary zinc and copper among a group of adults aged (19-22) years. Materials and methods: After examination eighty persons aged 19-22 years of both gender. Caries severity was documented according to DMFS index. Stimulated salivary samples were collected and chemically analyzed under standardized condition to detect salivary elements zinc and copper. Concentrations of Zinc and copper were measured by using
... Show MoreRecently, with the development multimedia technologies and wireless telecommunication, Voice over Internet Protocol, becomes widely used in communication between connecting people, VoIP allows people that are connected to the local network or the Internet to make voice calls using digital connection instead of based on the analog traditional telephone network. The technologies of Internet doesn’t give any security mechanism and there is no way to guarntee that the voice streams will be transmitted over Internet or network have not been intercepted in between. In this paper, VoIP is developed using stream cipher algorithm and the chaotic cryptography for key generator. It is based on the chaotic maps for generating a one-time rando
... Show MoreCompression of speech signal is an essential field in signal processing. Speech compression is very important in today’s world, due to the limited bandwidth transmission and storage capacity. This paper explores a Contourlet transformation based methodology for the compression of the speech signal. In this methodology, the speech signal is analysed using Contourlet transformation coefficients with statistic methods as threshold values, such as Interquartile Filter (IQR), Average Absolute Deviation (AAD), Median Absolute Deviation (MAD) and standard deviation (STD), followed by the application of (Run length encoding) They are exploited for recording speech in different times (5, 30, and 120 seconds). A comparative study of performance
... Show MoreMany neuroscience applications, including understanding the evolution of the brain, rely on neural cell instance segmentation, which seeks to integrate the identification and segmentation of neuronal cells in microscopic imagery. However, the task is complicated by cell adhesion, deformation, vague cell outlines, low-contrast cell protrusion structures, and background imperfections. On the other hand, existing segmentation approaches frequently produce inaccurate findings. As a result, an effective strategy for using the residual network with attention to segment cells is suggested in this paper. The segmentation mask of neural cells may be accurately predicted. This method is built on U-net, with EfficientNet serving as the e
... Show MoreClassifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area. The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and
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