Search results for: Malay speech recognition

Authors: Mehdi Ghayoumi

Abstract:

We have applied new accelerated algorithm for linear discriminate analysis (LDA) in face recognition with support vector machine. The new algorithm has the advantage of optimal selection of the step size. The gradient descent method and new algorithm has been implemented in software and evaluated on the Yale face database B. The eigenfaces of these approaches have been used to training a KNN. Recognition rate with new algorithm is compared with gradient.

Keywords: lda, adaptive, svm, face recognition.

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Authors: Mohsen Zand, Ahmadreza Naghsh Nilchi, S. Amirhassan Monadjemi

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Optical character recognition of cursive scripts presents a number of challenging problems in both segmentation and recognition processes in different languages, including Persian. In order to overcome these problems, we use a newly developed Persian word segmentation method and a recognition-based segmentation technique to overcome its segmentation problems. This method is robust as well as flexible. It also increases the system-s tolerances to font variations. The implementation results of this method on a comprehensive database show a high degree of accuracy which meets the requirements for commercial use. Extended with a suitable pre and post-processing, the method offers a simple and fast framework to develop a full OCR system.

Keywords: OCR, Persian, Recognition, Segmentation.

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Authors: S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha

Abstract:

Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey.

Keywords: Artificial intelligence, machine learning, deep learning, machine learning algorithms, machine learning tools.

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Authors: Armin Ghayoori, Faramarz Hendessi, Asrar Sheikh

Abstract:

In developing a text-to-speech system, it is well known that the accuracy of information extracted from a text is crucial to produce high quality synthesized speech. In this paper, a new scheme for converting text into its equivalent phonetic spelling is introduced and developed. This method is applicable to many applications in text to speech converting systems and has many advantages over other methods. The proposed method can also complement the other methods with a purpose of improving their performance. The proposed method is a probabilistic model and is based on Smooth Ergodic Hidden Markov Model. This model can be considered as an extension to HMM. The proposed method is applied to Persian language and its accuracy in converting text to speech phonetics is evaluated using simulations.

Keywords: Hidden Markov Models, text, synthesis.

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Authors: Nima Hatami

Abstract:

In this paper, we use Radial Basis Function Networks (RBFN) for solving the problem of environmental interference cancellation of speech signal. We show that the Second Order Thin- Plate Spline (SOTPS) kernel cancels the interferences effectively. For make comparison, we test our experiments on two conventional most used RBFN kernels: the Gaussian and First order TPS (FOTPS) basis functions. The speech signals used here were taken from the OGI Multi-Language Telephone Speech Corpus database and were corrupted with six type of environmental noise from NOISEX-92 database. Experimental results show that the SOTPS kernel can considerably outperform the Gaussian and FOTPS functions on speech interference cancellation problem.

Keywords: Environmental interference, interference cancellation of speech, Radial Basis Function networks, Gaussian and TPS kernels.

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Authors: Akhil Kumar Arya, Jagdeep Singh Lather, Lillie Dewan

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In this paper is to evaluate audio and speech quality with the help of Digital Audio Watermarking Technique under the different types of attacks (signal impairments) like Gaussian Noise, Compression Error and Jittering Effect. Further attacks are considered as Hostile Environment. Audio and Speech Quality Evaluation is an important research topic. The traditional way for speech quality evaluation is using subjective tests. They are reliable, but very expensive, time consuming, and cannot be used in certain applications such as online monitoring. Objective models, based on human perception, were developed to predict the results of subjective tests. The existing objective methods require either the original speech or complicated computation model, which makes some applications of quality evaluation impossible.

Keywords: Digital Watermarking, DCT, Speech Quality, Attacks.

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Authors: Gulzar A. Khuwaja, Mohammad S. Laghari

Abstract:

Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter. Signature verification systems can be categorized as offline (static) and online (dynamic). This paper presents a neural network based recognition of offline handwritten signatures system that is trained with low-resolution scanned signature images.

Keywords: Pattern Recognition, Computer Vision, AdaptiveClassification, Handwritten Signature Recognition.

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Authors: M. Almas Anjum, M. Younus Javed, A. Basit

Abstract:

In this paper a new approach to face recognition is presented that achieves double dimension reduction, making the system computationally efficient with better recognition results and out perform common DCT technique of face recognition. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results change with change in face image resolution and provide optimal results when arriving at a certain resolution level. In the proposed model of face recognition, initially image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to increased computational speed and feature extraction potential of Discrete Cosine Transform (DCT), it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A tradeoff between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL , Yale and EME color database.

Keywords: Biometrics, DCT, Face Recognition, Illumination, Computation, Feature extraction.

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

Abstract:

The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: Emotion recognition, facial recognition, signal processing, machine learning.

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Authors: Е. V. Kutsina, S. A. Tarasova

Abstract:

In this article we present a methodology which enables preschool and primary school unlanguaged children to remember words, phrases and texts with the help of graphic signs - letters, syllables and words. Reading for a child becomes a support for speech development. Teaching is based on the principle "from simple to complex", "a letter - a syllable - a word - a proposal - a text." Availability of multi-level texts allows using this methodology for working with children who have different levels of speech development.

Keywords: Alalia, analytic-synthetic method, development of coherent speech, formation of vocabulary, learning to read, , sentence formation, three-level stories, unlanguaged children.

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Authors: Ramin Halavati, Mansour Jamzad, Mahdieh Soleymani

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Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.

Keywords: Image Processing, Pattern Recognition.

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Authors: Laila Y. Fannas, Ahmed Y. Ben Sasi

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This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

Keywords: Signature Recognition, Artificial Neural Network, Angle Features.

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Authors: Moulay A. Akhloufi, Abdelhakim Bendada

Abstract:

In this work we present an efficient approach for face recognition in the infrared spectrum. In the proposed approach physiological features are extracted from thermal images in order to build a unique thermal faceprint. Then, a distance transform is used to get an invariant representation for face recognition. The obtained physiological features are related to the distribution of blood vessels under the face skin. This blood network is unique to each individual and can be used in infrared face recognition. The obtained results are promising and show the effectiveness of the proposed scheme.

Keywords: Face recognition, biometrics, infrared imaging.

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Authors: Edward Guillen, Daniel Padilla, Adriana Hernandez, Kenneth Barner

Abstract:

Biometrics methods include recognition techniques such as fingerprint, iris, hand geometry, voice, face, ears and gait. The gait recognition approach has some advantages, for example it does not need the prior concern of the observed subject and it can record many biometric features in order to make deeper analysis, but most of the research proposals use high computational cost. This paper shows a gait recognition system with feature subtraction on a bundle rectangle drawn over the observed person. Statistical results within a database of 500 videos are shown.

Keywords: Autentication, Biometrics, Gait Recognition, Human Identification, Security.

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Authors: Harish Chander, Balwinder Singh, Ravinder Khanna

Abstract:

In this paper, we present the comparative subjective analysis of Improved Minima Controlled Recursive Averaging (IMCRA) Algorithm, the Kalman filter and the cascading of IMCRA and Kalman filter algorithms. Performance of speech enhancement algorithms can be predicted in two different ways. One is the objective method of evaluation in which the speech quality parameters are predicted computationally. The second is a subjective listening test in which the processed speech signal is subjected to the listeners who judge the quality of speech on certain parameters. The comparative objective evaluation of these algorithms was analyzed in terms of Global SNR, Segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) by the authors and it was reported that with cascaded algorithms there is a substantial increase in objective parameters. Since subjective evaluation is the real test to judge the quality of speech enhancement algorithms, the authenticity of superiority of cascaded algorithms over individual IMCRA and Kalman algorithms is tested through subjective analysis in this paper. The results of subjective listening tests have confirmed that the cascaded algorithms perform better under all types of noise conditions.

Keywords: Speech enhancement, spectral domain, time domain, PESQ, subjective analysis, objective analysis.

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Authors: Sergei Aleinik, Yuri Matveev

Abstract:

In this paper a novel method for the detection of  clipping in speech signals is described. It is shown that the new  method has better performance than known clipping detection  methods, is easy to implement, and is robust to changes in signal  amplitude, size of data, etc. Statistical simulation results are  presented.

 

Keywords: Clipping, clipped signal, speech signal processing.

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Authors: Liming Zhang

Abstract:

In this paper, a new adaptive Fourier decomposition (AFD) based time-frequency speech analysis approach is proposed. Given the fact that the fundamental frequency of speech signals often undergo fluctuation, the classical short-time Fourier transform (STFT) based spectrogram analysis suffers from the difficulty of window size selection. AFD is a newly developed signal decomposition theory. It is designed to deal with time-varying non-stationary signals. Its outstanding characteristic is to provide instantaneous frequency for each decomposed component, so the time-frequency analysis becomes easier. Experiments are conducted based on the sample sentence in TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results show that the AFD based time-frequency distribution outperforms the STFT based one.

Keywords: Adaptive fourier decomposition, instantaneous frequency, speech analysis, time-frequency distribution.

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Authors: Vladimir A. Fursov, Nikita E. Kozin

Abstract:

In this work the opportunity of construction of the qualifiers for face-recognition systems based on conjugation criteria is investigated. The linkage between the bipartite conjugation, the conjugation with a subspace and the conjugation with the null-space is shown. The unified solving rule is investigated. It makes the decision on the rating of face to a class considering the linkage between conjugation values. The described recognition method can be successfully applied to the distributed systems of video control and video observation.

Keywords: Conjugation, Eigenfaces, Recognition.

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Authors: Elizabeth B. Varghese, M. Wilscy

Abstract:

A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: R. Rishma Rodrigo, R. Radhika, M. Vanitha Lakshmi

Abstract:

Numerous signal processing based speech enhancement systems have been proposed to improve intelligibility in the presence of noise. Traditionally, studies of neural vowel encoding have focused on the representation of formants (peaks in vowel spectra) in the discharge patterns of the population of auditory-nerve (AN) fibers. A method is presented for recording high-frequency speech components into a low-frequency region, to increase audibility for hearing loss listeners. The purpose of the paper is to enhance the formant of the speech based on the Kaiser window. The pitch and formant of the signal is based on the auto correlation, zero crossing and magnitude difference function. The formant enhancement stage aims to restore the representation of formants at the level of the midbrain. A MATLAB software’s are used for the implementation of the system with low complexity is developed.

Keywords: Formant estimation, formant enhancement, pitch detection, speech analysis.

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Authors: Zaharani Ahmad, Nor Hashimah Jalaluddin

Abstract:

The aim of this paper is to examine and identify the issue of linguistic redundancy in two competing grammars of Malay, namely the school grammar and the corpus grammar. The former is a normative grammar which is formally and prescriptively taught in the classroom, whereas the latter is a descriptive grammar that is informally acquired and mastered by the students as native speakers of the language outside the classroom. Corpus grammar is depicted based on its actual used in natural occurring texts, as attested in the corpus. It is observed that the grammar taught in schools is incompatible with the grammar used in the corpus. For instance, a noun phrase containing nominal reduplicated form which denotes plurality (i.e. murid-murid ‘students’ which is derived from murid ‘student’) and a modifier categorized as quantifiers (i.e. semua ‘all’, seluruh ‘entire’, and kebanyakan ‘most’) is not acceptable in the school grammar because the formation (i.e. semua murid-murid ‘all the students’ kebanyakan pelajar-pelajar ‘most of the students’) is claimed to be redundant, and redundancy is prohibited in the grammar. Redundancy is generally construed as the property of speech and language by which more information is provided than is precisely required for the message to be understood, so that, if some information is omitted, the remaining information will still be sufficient for the message to be comprehended. Thus, the correct construction to be used is strictly the reduplicated form (i.e. murid-murid ‘students’) or the quantifier plus the root (i.e. semua murid ‘all the students’) with the intention that the grammatical meaning of plural is not repeated. Nevertheless, the so-called redundant form (i.e. kebanyakan pelajar-pelajar ‘most of the students’) is frequently used in the corpus grammar. This study shows that there are a number of redundant forms occur in the morphology of the language, particularly in affixation, reduplication and combination of both. Apparently, the so-called redundancy has grammatical and socio-cultural functions in communication that is to give emphasis and to stress the importance of the information delivered by the speakers or writers.

Keywords: Corpus grammar, morphology, redundancy, school grammar.

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Authors: Sevil Igit, Merve Meric, Sarp Erturk

Abstract:

In this paper, it is proposed to improve Daisy Descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach.

Keywords: Face Recognition, Daisy Descriptor, One-Bit Transform, Image Registration.

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Authors: Ahmad T. Al-Taani

Abstract:

In this paper, an efficient structural approach for recognizing on-line handwritten digits is proposed. After reading the digit from the user, the slope is estimated and normalized for adjacent nodes. Based on the changing of signs of the slope values, the primitives are identified and extracted. The names of these primitives are represented by strings, and then a finite state machine, which contains the grammars of the digits, is traced to identify the digit. Finally, if there is any ambiguity, it will be resolved. Experiments showed that this technique is flexible and can achieve high recognition accuracy for the shapes of the digits represented in this work.

Keywords: Digits Recognition, Pattern Recognition, FeatureExtraction, Structural Primitives, Document Processing, Handwritten Recognition, Primitives Selection.

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Authors: Treepon Kirdnark

Abstract:

This paper examines the depiction of Muslim militants in Thai newspapers in 2004. Stuart Hall-s “representation" and “public idioms" are used as theoretical frameworks. Critical Discourse Analysis is employed as a methodology to examine 240 news articles from two leading Thai language newspapers. The results show that the militants are usually labeled as “southern bandits." This suggests that they are just a culprit of the violence in the deep south of Thailand. They are usually described as people who cause turbulence. Consequently, the military have to get rid of them. However, other aspects of the groups such as their political agenda or the failures of the Thai state in dealing with the Malay Muslims were not mention in the news stories. In the time of violence, the researcher argues that this kind of newspaper coverage may help perpetuate the discourse of Malay Muslim, instead of providing fuller picture of the ongoing conflicts.

Keywords: News Discourse, Newspapers, Thailand, Thai Muslims.

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Authors: J. Umamaheswari, G. Radhamani

Abstract:

This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

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Authors: M. A Anjum, M. Y. Javed, A. Basit

Abstract:

In this paper a new approach to face recognition is presented that achieves double dimension reduction making the system computationally efficient with better recognition results. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results improve with increase in face image resolution and levels off when arriving at a certain resolution level. In the proposed model of face recognition, first image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to better computational speed and feature extraction potential of Discrete Cosine Transform (DCT) it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A trade of between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL database, Yale database and a color database. The proposed technique has performed much better compared to other techniques. The significance of the model is two fold: (1) dimension reduction up to an effective and suitable face image resolution (2) appropriate DCT coefficients are retained to achieve best recognition results with varying image poses, intensity and illumination level.

Keywords: Biometrics, DCT, Face Recognition, Feature extraction.

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Authors: Moulay A. Akhloufi, Abdelhakim Bendada

Abstract:

Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA and LDA. In this work, we introduce a probabilistic Bayesian framework for face recognition in the infrared spectrum. In the infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. Bayesian approaches permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. This framework is compared with classical linear techniques. Non linear techniques we developed recently for infrared face recognition are also presented and compared to the Bayesian face recognition framework. A new approach for infrared face extraction based on SVM is introduced. Experimental results show that the Bayesian technique is promising and lead to interesting results in the infrared spectrum when a sufficient number of face images is used in an intrapersonal learning process.

Keywords: Face recognition, biometrics, probabilistic imageprocessing, infrared imaging.

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Authors: Mangesh S. Deshpande, Raghunath S. Holambe

Abstract:

Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable for speaker features that are located in high frequency regions. The speaker individual information, which is non-uniformly distributed in the high frequencies, is equally important for speaker recognition. Based on this fact we proposed an admissible wavelet packet based filter structure for speaker identification. Multiresolution capabilities of wavelet packet transform are used to derive the new features. The proposed scheme differs from previous wavelet based works, mainly in designing the filter structure. Unlike others, the proposed filter structure does not follow Mel scale. The closed-set speaker identification experiments performed on the TIMIT database shows improved identification performance compared to other commonly used Mel scale based filter structures using wavelets.

Keywords: Speaker identification, Wavelet transform, Feature extraction, MFCC, GMM.

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Authors: Anna Mirzaiyan, Vahid Parvaresh, Mahmoud Hashemian, Masoud Saeedi

Abstract:

People usually have a telephone voice, which means they adjust their speech to fit particular situations and to blend in with other interlocutors. The question is: Do we speak differently to different people? This possibility has been suggested by social psychologists within Accommodation Theory [1]. Converging toward the speech of another person can be regarded as a polite speech strategy while choosing a language not used by the other interlocutor can be considered as the clearest example of speech divergence [2]. The present study sets out to investigate such processes in the course of everyday telephone conversations. Using Joos-s [3] model of formality in spoken English, the researchers try to explore convergence to or divergence from the addressee. The results propound the actuality that lexical choice, and subsequently, patterns of style vary intriguingly in concordance with the person being addressed.

Keywords: Convergence, divergence, lexical formality, speechaccommodation.

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Authors: P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, C. Kimpan

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Much research into handwritten Thai character recognition have been proposed, such as comparing heads of characters, Fuzzy logic and structure trees, etc. This paper presents a system of handwritten Thai character recognition, which is based on the Ant-minor algorithm (data mining based on Ant colony optimization). Zoning is initially used to determine each character. Then three distinct features (also called attributes) of each character in each zone are extracted. The attributes are Head zone, End point, and Feature code. All attributes are used for construct the classification rules by an Ant-miner algorithm in order to classify 112 Thai characters. For this experiment, the Ant-miner algorithm is adapted, with a small change to increase the recognition rate. The result of this experiment is a 97% recognition rate of the training set (11200 characters) and 82.7% recognition rate of unseen data test (22400 characters).

Keywords: Hand written, Thai character recognition, Ant-mineralgorithm, distinct feature.

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