Automatic Identity Recognition Using Speech Biometric

Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recognizer using speech biometric technology based on Artificial Intelligence (AI) techniques provided in MATLAB. For our phase one, speech data is collected from 20 (10 male and 10 female) participants in order to develop the recognizer. The speech data include utterances recorded for the English language digits (0 to 9), where each participant recorded each digit 3 times, which resulted in a total of 600 utterances for all participants. For our phase two, speech data is collected from 100 (50 male and 50 female) participants in order to develop the recognizer. The speech data is divided into text-dependent and text-independent data, whereby each participant selected his/her full name and recorded it 30 times, which makes up the text-independent data. On the other hand, the text-dependent data is represented by a short Arabic language story that contains 16 sentences, whereby every sentence was recorded by every participant 5 times. As a result, this new corpus contains 3000 (30 utterances * 100 speakers) sound files that represent the text-independent data using their full names and 8000 (16 sentences * 5 utterances * 100 speakers) sound files that represent the text-dependent data using the short story. For the purpose of our phase one of developing the automatic identity recognizer using speech, the 600 utterances have undergone the feature extraction and feature classification phases. The speech-based automatic identity recognition system is based on the most dominating feature extraction technique, which is known as the Mel-Frequency Cepstral Coefficient (MFCC). For feature classification phase, the system is based on the Vector Quantization (VQ) algorithm. Based on our experimental results, the highest accuracy achieved is 76%. The experimental results have shown acceptable performance, but can be improved further in our phase two using larger speech data size and better performance classification techniques such as the Hidden Markov Model (HMM)

Motivations to Enroll in Education Graduate Programs in Jordan: A Qualitative Field Study at Yarmouk University

الملخص: هدفت الدراسة الحالية التعرف على أسباب ودوافع التحاق طلبة جامعة اليرموك ببرامج الدراسات العليا للتخصصات التربوية في الأردن، شارك في الدراسة عشرة من الطلبة (6) منهم ذكور و(4) إناث وقد استخدم في الدراسة البحث النوعي من خلال أسلوب المقابلة المعمقة حيث تم مقابلة المشاركين من الطلبة الملتحقين ببرامج الدكتوراه والماجستير في التخصصات التربوية بجامعة اليرموك، وتم طرح أسئلة متفرقة في كل جلسة، تحتمل إجابات مفتوحة ومعمقة لإعطاء معنى للظاهرة من وجهة نظر المشاركين بها. نتج عن تحليل استجابات المشاركين بتركيزها على الدوافع الآتية: التقدم الوظيفي، وإيجاد فرص عمل أوسع، والدافع الاجتماعي الذي يركز على تحسين المكانة الاجتماعية، وتكوين علاقات اجتماعية، وتشجيع الأهل والأصدقاء لمتابعة الدراسة، ثم تلاه الدافع الاقتصادي الذي ينظر إلى أن الحصول على شهادة عليا سيزيد في مستوى دخل الفرد، والترقية الوظيفية، وتحسين الظروف المعيشية الشخصية، والحصول على فرص عمل في الخارج. كما أشارت النتائج إلى دور الدافع النفسي للالتحاق بكلية الدراسات العليا والذي يعمل على توفير الاستقرار والرضا النفسي، وتحقيق الذات. كما أن هناك إشارة إلى سياسات القبول في الجامعات حيث أصبحت أكثر مرونة من السابق، وتغيرت المعايير، وهذا فتح الباب على مصراعيه للقبول، مما شجع الطلبة على تزايد الإقبال، وأضافوا إلى أن مستقبل الدراسات العليا واضح في ضوء هذه المعايير والسياسات، حيث سيتزايد الطلبة وسوف يتم فتح تخصصات أخرى في المستقبل. الكلمات المفتاحية: دوافع الإقبال، برامج، الدراسات العليا، التخصصات التربوية، دراسة نوعية، جامعة اليرموك، الأردن.The present study aimed to identify the causes and motives of Yarmouk University students to enroll in graduate programs of educational disciplines in Jordan. Ten students (6 males and 4 females) participated in the study. The study used qualitative research method by means of in-depth interviews during which the participants were given various open questions in each session so as to express their views about the issue under investigation. After the analysis of participants’ responses, it was found that they focused on the following motives: career advancement; creation of wider job opportunities; social motivation, which focuses on improving the social status; formation of social relationships; friends and family’s encouragement to pursue graduate studies. Then economic motives were brought in by the participants, who believed that to get a graduate degree would increase their income, career promotion, personal living standards, and job opportunities abroad. The results also pointed to the role of psychological motivation for admission to the College of Graduate Studies. This would provide psychological stability and satisfaction, and self-esteem. There was also a reference to the admission policies at universities which became more flexible than before, whereby standards have also changed. This made admission more open than before, which encouraged more students to apply to graduate programs. They also added that in view of these standards and policies, it is clear that more students will apply to the graduate programs in the future; and other new disciplines would be opened in the future as well. Keywords: Motivations of enrollment, Graduate studies, Programs, Educational disciplines, Qualitative study, Yarmouk University, Jordan

Automatic identity recognition systems : a review

Rapidly changed computer technology and fast growth of communication ways, makes everyday work easy and managed. Technology takes place everywhere, in business, education, market, security... etc. However, communication between human and these technologies become the main concern of many research areas, especially for developing automatic identity recognition systems. However, biometric technologies are among the most important technologies used in this area. Biometric technology refers to the automatic identity recognition using physical or behavioral traits associated with him/her. Using biometrics, it is possible to establish physiological-based systems that depend on physiological characteristics such as fingerprint, face recognition, DNA... etc, or behavioral-based systems that depend on behavioral characteristics such as gait, voice ...etc, or even combining both of them in one system. Therefore, biometrics technologies can be excellent candidates for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. In addition, biometric technologies are flexible enough to be combined with other tools to produce more secure and easier to use verification system

Arabic automatic continuous speech recognition systems

MSA is the current formal linguistic standard of Arabic language, which is widely taught in schools and universities, and often used in the office and the media. MSA is also considered as the only acceptable form of Arabic language for all native speakers [I]. As recently, the research community has witnessed an improvement in the performance of ASR systems, there is an increasingly widespread use of this technology for several languages of the world. Similarly, research interests have grown significantly in the past few years for Arabic ASR research. It is noticed that Arabic ASR research is not only conducted and investigated by researchers in the Arab world, but also by many others located in different parts of the \vorld especially the western countries

Speaker’s variabilities, technology and language issues that affect automatic speech and speaker recognition systems

Automatic Speech Recognition (ASR) is gammg its importance due to the vast growth generally in technology and computing in specific. From industrial perspective, computers, laptops, and mobile devices nowadays have the ASR support embedded into the operating system. From academia on the other hand, there are many research efforts being conducted addressing this technology in order to contribute to its state-of-the-art. On the other hand, speaker recognition systems are also growing due to various threats, therefore, these systems are mostly meant for security purpose