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 aim of this research is to collect the semantically restricted vocabulary from linguistic vocabulary and make it regular in one wire with an in-depth study. This study is important in detecting the exact meanings of the language. On the genre, as shown in this research, and our purpose to reveal this phenomenon, where it shows the accuracy of Arabic in denoting the meanings, the research has overturned more than sixty-seven words we extracted from the stomachs of the glossaries and books of language, and God ask safety intent and payment of opinion.
Background: Chief complaint of patients attending dental clinic represents the first step towards treatment plan. However, most of patients are not aware but the extent and severity of periodontal disease, which could be also, misdiagnose by the dentist. Aim of the study: To investigate whether reported chief complaint(s) are consistent with oral hygiene status Materials and methods: Records of 1102 patients, attending periodontics clinics in the college of dentistry/ university of Baghdad, were used to determine ten most commonly reported chief complaints. Sample of patients was further subdivided according to gender and age. In addition, plaque and gingival index were recorded to determine oral hygiene status. Results: Patients mostly
... Show MoreGender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
... Show MoreDr. Qahtan Al-Madfa’i’s architecture has been characterized by a particular characteristic that may be unique and extreme at the same time, that is the use of the distinctive three-dimensional structural coverings and the exploitation of structural construction to give an extra aesthetic touch to the composition of the building, to achieve the application of his universal ideas, which he strongly believed and defended.
In the period of the marked urban decline that the country undergoes now, which urges us toward making a comparison between the beginning of the modern Iraqi architecture and its ascending path up to its peak and the periods of its decline until it reached a very
... Show MorePiracy on phonograms is now, rightly, the crime of the electronic age. Despite the protection sought by States to provide for such registrations, whether at the level of national legislation or international agreements and conventions, but piracy has been and continues to pose a significant threat to the rights of the producers of those recordings, especially as it is a profitable way for hackers to get a lot of money in a way Illegal, which is contrary to the rules of legitimate competition. Hence, this research highlights the legal protection of producers of phonograms in light of the Iraqi Copyright Protection Act No. (3) of 1971, as amended.
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
... Show MoreThis paper is devoted to investigate the effect of internal curing technique on the properties of self-compacting concrete (SCC). In this study, SCC is produced by using silica fume (SF) as partial replacement by weight of cement with percentage of (5%), sand is partially replaced by volume with saturated fine lightweight aggregate (LWA) which is thermostone chips as internal curing material in three percentages of (5%, 10% and 15%) for SCC, two external curing conditions water and air. The experimental work was divided into three parts: in the first part, the workability tests of fresh SCC were conducted. The second part included conducting compressive strength test and modulus of rupture test at ages of (7, 28 and 90). The third part i
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