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)

A proposed model for Quranic Arabic WordNet

Most recent Arabic language computing research focuses on modern standard Arabic, but the classical Arabic of the Qur’an has been relatively unexplored, despite the importance of the Qur’an to Islam worldwide which can be used by both scholars and learners.This research work proposes to develop a WordNet for Qur’an by building semantic connections between words in order to achieve a better understanding of the meanings of the Qur’anic words using traditional Arabic dictionaries and a Qur’an ontology. The Qur’an corpus will be used as text and Boundary Annotated Qur’an Corpus (Brierley et al, 2012) will be used to explore the root and Part-of-Speech for each word and the word by word English translations. Traditional Arabic dictionaries will be used to find the Arabic meaning and derived words for each root in the Qur'anic Corpus. Then, these words and their meanings (Arabic, English) will be connected together through semantic relations. The achieved Qur’anic WordNet will provide an integrated semantic Qur’anic dictionary for the Arabic and English versions of the Qur'an

English digits speech recognition system based on hidden Markov Models

The field of Automatic Speech Recognition (ASR) is about 60 years old. There have been many interesting advances and developments since the invention of the first speech recognizer at Bell Labs in the early 1950' s. The development of ASR increased gradually until the invention of Hidden Markov Models (HMM) in early 1970's. Researchers' contribution were to make use of ASR technology to what can be seen nowadays of various advancements in fields like multi-modal, multi-linguaVcross-lingual ASR using statistical techniques such as HMM, SVM, neural network, etc [1]

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

Al-Quran learning using mobile speech recognition:an overview

The usage of mobile application in various aspects has been worldwide accepted and there are variety of mobile applications which developed to cater the usage of different background of the user. In this paper, a short survey which includes questionnaire is distributed to find the interest of user whom using application for learning Quran and concept of mobile speech apps. The main interest of this survey is to find the acceptance of user and explanation on the proposed usage of mobile speech recognition with feature of learning apps. Factors of mobile speech recognition and mobile learning are listed to support the results from the short survey

Speech recognition system using MATLAB : design, implementation, and samples codes

Research in automatic speech recognition has been done for almost four decades. Over the past decades, the development of speech recognition applications gives invaluable contributions. Speech has the potential to be a better interface than other computing devices used such as keyboard or mouse. This project aims to develop automated English digits speech recognition system. The project relies heavily on the well known and widely used statistical method in characterizing the speech pattern, the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. This project discusses the theory of HMM and then extends the ideas to the development and implementation by applying this method in computational speech recognition. Basically, the system is able to recognize the spoken utterances by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique, which then estimates the observation likelihood by using the Forward algorithm. The HMM parameters are estimated by applying the Baum Welch algorithm on previously trained samples. The most likely sequence is then decoded using Viterbi algorithm, thus producing the recognized word. This project focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed relatively successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 70% whereas the speaker-independent mode achieved 55%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 92.5% whereas the speaker-independent mode achieved 75%. These recognition rates are relatively successful if compared to similar systems

Acoustic echo cancellation using adaptive filtering algorithms for quranic accents (Qiraat) identification

Echoed parts of Quranic accent (Qiraat) signals are exposed to reverberation of signals especially if they are listened to in a conference room or the Quranic recordings found in different media such as the web. Quranic verse rules identification/Tajweed are prone to additive noise and may reduce classification results. This research work aims to present our work towards Quranic accents (Qiraat) identification, which emphasizes on acoustic echo cancellation (AEC) of all echoed Quranic signals during the preprocessing phase of the system development. In order to conduct the AEC, three adaptive algorithms known as affine projection (AP), least mean square (LMS), and recursive least squares (RLS) are used during the preprocessing phase. Once clean Quranic signals are produced, they undergo feature extraction and pattern classification phases. The Mel Frequency Cepstral Coefficients is the most widely used technique for feature extraction and is adopted in this research work, whereas probabilities principal component analysis (PPCA), K-nearest neighbor (KNN) and gaussian mixture model (GMM) are used for pattern classification. In order to verify our methodology, audio files have been collected for Surat Ad-Duhaa for five different Quranic accents (Qiraat), namely: (1) Ad-Duri, (2) Al-Kisaie, (3) Hafs an A’asem, (4) IbnWardan, and (5) Warsh. Based on our experimental results, the AP algorithm achieved 93.9 % accuracy rate against all pattern classification techniques including PPCA, KNN, and GMM. For LMS and RLS, the achieved accuracy rates are different for PPCA, KNN, and GMM, whereby LMS with PPCA and GMM achieved the same accuracy rate of 96.9 %; however, LMS with KNN achieved 84.8 %. In addition, RLS with PPCA and GMM achieved the same accuracy rate of 90.9 %; however, RLS with KNN achieved 78.8 %. Therefore, the AP adaptive algorithm is able to reduce the echo of Quranic accents (Qiraat) signals in a consistent manner against all pattern classification techniques

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

Signature recognition using artificial neural network

Nowadays, there are many applications required the user to confirm his identity. It might be done by asking a secret question that the user will answer to get into that application, and it might be a password or a pin code, face, eye, fingerprint or signature. Automatic signature verification is an active field of research with many practical applications. Automatic handwritten signature verification is divided into two approaches: off-line and on-line. In the off-line signature verification approach, the data of the signature is obtained from a static image utilizing a scanning device [I). For our application, off-line approach will be utilized.Neural Networks (NN) also known as Artificial Neural Networks (ANN) belong to the artificial intelligence approaches, which attempt to mechanize the recognition procedure according to the way a person applies intelligence in visualizing and analyzing[2]. Neural Networks' structure is inspired by biological models of the nervous system proposed as a model of the human brain's activities aiming to mimic certain processing capabilities of the human brain