scholarly journals Theories that develop

2002 ◽  
Vol 5 (3) ◽  
pp. 216-217 ◽  
Author(s):  
Michael S. C. Thomas
Keyword(s):  
Word Recognition ◽  
Computational Models ◽  
Context Effects ◽  
Recognition System ◽  
Significant Advance ◽  
Target Article ◽  
Simplifying Assumptions ◽  
Recognition Systems ◽  
Decision Mechanisms ◽  
New Framework

The target article represents a significant advance in the level of sophistication applied to models of bilingual word recognition, and Dijkstra and van Heuven are to be congratulated on this endeavour. Bearing in mind the success of the (computational) BIA model in capturing detailed patterns of experimental data, I look forward to future simulation results from the BIA+ when the proposals of this new framework are implemented. It is an essential step to draw a distinction between recognition systems and the decision mechanisms that drive responses, and the authors have provided a novel way of apportioning empirical evidence of context effects in bilingual word recognition across this divide. Given the explanatory weight now being placed on decision mechanisms rather than the word recognition system itself, perhaps indeed it is now time to make some simplifying assumptions about the recognition system and start building detailed computational models of the decision component of the system. Implementation will provide the clarity of theorisation and evaluation of theory viability that have been the hallmark of the BIA model thus far.

Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

Design and Realization of On-Line Uyghur Handwritten Character Collection System

2014 ◽  
Vol 989-994 ◽  
pp. 4742-4746
Author(s):  
Halmurat Dilmurat ◽  
Kurban Ubul
Keyword(s):  
Data Collection ◽  
Word Recognition ◽  
Character Recognition ◽  
Recognition System ◽  
Collection System ◽  
System A ◽  
Handwritten Character ◽  
On Line ◽  
Recognition Systems ◽  
Whole Systems

Data collection is the first step in handwritten character recognition systems, and the data quality collected effects the whole systems efficiency. As the necessary subsystem of on-line handwritten character/word recognition system, a Uyghur handwritten character collection system is designed and implemented with Visual C++ based on the nature of Uyghur handwriting. Uyghur handwritings is encoded by 8 direction tendency and stored in extension stroke file. And they are collected based on the content of Text Prompt File. From experimental results, it can be concluded that the handwriting collection system indicates its strong validity and efficiency during the collection of Uyghur handwriting.

scholarly journals On the temporal delay assumption and the impact of non-linguistic context effects

2002 ◽  
Vol 5 (3) ◽  
pp. 199-201 ◽  
Author(s):  
Marc Brysbaert ◽  
Ilse van Wijnendaele ◽  
Wouter Duyck
Keyword(s):  
Word Recognition ◽  
Context Effects ◽  
Identification System ◽  
Temporal Delay ◽  
Linguistic Context ◽  
Cautionary Note ◽  
Research Areas ◽  
Target Article ◽  
The Impact ◽  
Selective Access

It is not easy to comment on Dijkstra and Van Heuven's model because there are many more aspects we agree with than aspects we feel uncomfortable about. Indeed, the BIA model has played an enormous role in showing us how bilingual visual word recognition can be achieved without recurrence to the intuitively appealing – but wrong – ideas of separate, language-specific lexicons and language-selective access. As in many other research areas, a working computational model has been much more influential in convincing critical readers (and researchers) than any series of empirical findings. The BIA+ model inherits this strength and, hopefully, in the coming years will be implemented in enough detail to exceed its predecessor. In the rest of this comment, we would like to put a cautionary note behind the temporal delay assumption introduced in the target article and provide some additional corroborating evidence for the lack of non-linguistic effects on early processes in the identification system.

scholarly journals A New Framework For Identifying the Missed Pilgrims In Hajj and Umrah

2017 ◽  
Vol 10 (04) ◽  
pp. 718-724 ◽  
Author(s):  
Jawad AlKhateeb
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Probabilistic Neural Network ◽  
Recognition System ◽  
Recognition Systems ◽  
New Framework

A new framework recognition system for identifying the missed pilgrims in Hajj and Umrah is presented in this paper. The framework recognition system is based on the Wavelet Probabilistic Neural Network (WPNN) classifier. The entire framework recognition system is capable of identifying the missed pilgrims. The framework recognition systems is developed as an application for mobile phones The proposed framework recognition system is applied to a huge database for pilgrims in the Ministry of Hajj Computer server for matching and identifying the missed pilgrim. The results are superior comparing to the existing systems

Bilingual word recognition beyond orthography: On meaning, linguistic context and individual differences

2002 ◽  
Vol 5 (3) ◽  
pp. 209-212 ◽  
Author(s):  
Janet G. van Hell
Keyword(s):  
Word Recognition ◽  
Context Effects ◽  
Word Identification ◽  
Empirical Studies ◽  
Recognition System ◽  
Task Demands ◽  
Identification System ◽  
Linguistic Context ◽  
Decision System ◽  
Language Competition

Central questions in psycholinguistic studies on bilingualism are how bilinguals access words in their two languages, and how they control their language systems and solve the problem of cross-language competition. In their excellent paper “The architecture of the bilingual word recognition system: From identification to decision”, Dijkstra and Van Heuven expound their BIA+ model on bilingual word recognition. BIA+ builds on its predecessor BIA, one of the first connectionist models on bilingual word recognition. BIA+ preserves one of BIA's crucial assumptions, namely that the bilingual lexicon is integrated across languages and is accessed in a language non-selective way, an assumption that is supported in many empirical studies and that is now widely accepted in the bilingual literature. Compared to the original BIA model, the BIA+ architecture is further developed (in fact, much more so than the subtle ‘plus’ denotes). BIA+ now includes orthographic, as well as phonological and semantic representations in the word identification system, and a distinction is made between a word identification system and a task/decision system. This latter extension resembles the language task schemas in Green's (1998) Inhibitory Control model. Dijkstra and Van Heuven also distinguish between effects of linguistic and non-linguistic context on performance: linguistic context effects, that arise from lexical, syntactic and semantic sources, are assumed to affect the activity in the word identification system, whereas non-linguistic effects, that can arise from instruction, task demands or participant expectancies, are assumed to affect the task/decision system.

Does Viotin Activate Violin More Than Viocin?

2014 ◽  
Vol 61 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Manuel Perea ◽  
Victoria Panadero
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Developmental Dyslexia ◽  
Computational Models ◽  
Error Rates ◽  
Visual Word ◽  
Base Word ◽  
Similar Response ◽  
Skilled Readers ◽  
Young Readers

The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word’s overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children – this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word’s visual cues, presumably because of poor letter representations.

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages

2021 ◽  
Vol 20 (4) ◽  
pp. 1-19
Author(s):  
Manjunath K. E. ◽  
Srinivasa Raghavan K. M. ◽  
K. Sreenivasa Rao ◽  
Dinesh Babu Jayagopi ◽  
V. Ramasubramanian
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Window Size ◽  
Recognition System ◽  
Error Rates ◽  
Indian Languages ◽  
International Phonetic Alphabet ◽  
Phone Recognition ◽  
Front End ◽  
Recognition Systems

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammadreza Azimi ◽  
Seyed Ahmad Rasoulinejad ◽  
Andrzej Pacut
Keyword(s):  
Iris Recognition ◽  
Recognition Task ◽  
Aging Effect ◽  
Recognition System ◽  
Health Condition ◽  
Age Dependency ◽  
Biometric Systems ◽  
Kolmogorov Smirnov ◽  
Recognition Systems ◽  
Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

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