A New Step in Arabic Speech Identification: Spoken Digit Recognition

Saeed, Khalid; Nammous, Mohammad K.

doi:10.1007/0-387-26325-x_6

Cited by 7 publications

(5 citation statements)

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“…The presented modification combines a View-Based method with the algorithm of minimal eigenvalues of Töeplitz matrices previously used in cursive-character recognition [3], [11] as well as in spoken word recognition [15], [16]. It is certainly worth mentioning that the idea of Toeplitz matrices and their minimal eigenvalues were successfully used by researchers as a tool similar to discrete Fourier transform [17] or for studying the relationship between Karhunen-Loeve expansion and discrete cosine transform DCT [18] and also for computing the eigenvectors of uniformly rotated images [19].…”

Section: Discussionmentioning

confidence: 99%

A View-Based T¿eplitz-Matrix-Supported System for Word Recognition without Segmentation

Tabędzki

Saeed

2006

Sixth International Conference on Intelligent Systems Design and Applications

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In this paper, new modifications and experiments for word recognition and classification are presented. The algorithm is based on recognizing the whole words without separating them into letters. The whole word is treated and analyzed as an image. The method is based on the modification of a novel view-based word recognition algorithm -an approach that was successfully used by the authors' in previous works.This method shows how to recognize words without segmentation. The top and bottom views of the word are analyzed in order to create the feature vector. Then the feature vector is processed by the aid of Töeplitz matrices. The obtained series of Töeplitz matrix minimal eigenvalues are used for classification. The results are promising.

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Section: Discussionmentioning

confidence: 99%

A View-Based T¿eplitz-Matrix-Supported System for Word Recognition without Segmentation

Tabędzki

Saeed

2006

Sixth International Conference on Intelligent Systems Design and Applications

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“…( ) ( ), ( ), ..., ( ) , q q q qZ u t u t u t u t  (10) Where Z is the number of considered frames (each frame of 20 ms duration) for the th q WT sub signal ( ). q u t The average of LPC coefficients calculated for Z frames of ( ) q u t is utilized to extract wavelet sub signal feature vector as follows:…”

Section: Average Framing Lpc Feature Extraction Methodsmentioning

confidence: 99%

“…A new algorithm to recognize separate voices of some Arabic words was presented in [10], the digits from zero to ten were presented and compared. For feature extraction, transformation and hence recognition, the algorithm of minimal Eigen values of Toeplitz matrices along with other methods of speech processing and recognition were used to achieve a more accurate recognition rate of speaker-independent mode.…”

Section: Introductionmentioning

confidence: 99%

Wavelet LPC with Neural Network for Spoken Arabic Digits Recognition System

Daqrouq

2014

BJAST

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“…Based on that, it is a necessity to enable disabled students to interact with computers in a simple and effective way. Hence, multiple software solutions were proposed with different levels of efficiency and flexibility to create a near normal interaction real time environment between disabled students and computers . Accordingly, we were motivated in this work to present a speech recognition system that helps armed‐disabled students to communicate with computers for educational applications in a simple yet flexible way and with a high accuracy ratio in implementation.…”

Section: Introductionmentioning

confidence: 99%

“…No more subtle elements were accounted for these states and transitions. The authors in reference deals with the application of Toeplitz matrices and their minimal Eigen values together with a number of different types of neural networks on speech recognition. The speech signal is looked at as an image and it is treated graphically.…”

Section: Introductionmentioning

confidence: 99%

An efficient speech recognition system for arm‐disabled students based on isolated words

Darabkh

Haddad

Sweidan

et al. 2017

Comp Applic In Engineering

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Over the previous decades, a need has emerged to empower human‐machine communication systems, which are essential to not only perform actions, but also obtain information especially in education applications. Moreover, any communication system has to introduce an efficient and easy way for interaction with a minimum possible error rate. The keyboard, mouse, trackball, touch‐screen, and joystick are all examples of tools which were built to provide mechanical human‐to‐machine interaction. However, a system with the ability to use oral speech, which is the natural form of communication between humans instead of mechanical communication systems, can be more practical for normal students and even a necessity for arm‐disabled students who cannot use their arms to handle traditional education tools like pens and notebooks. In this paper, we present a speech recognition system that allows arm‐disabled students to control computers by voice as a helping tool in the educational process. When a student speaks through a microphone, the speech is divided into isolated words which are compared with a predefined database of huge number of spoken words to find a match. After that, each recognized word is translated into its related tasks which will be performed by the computer like opening a teaching application or renaming a file. The speech recognition process discussed in this paper involves two separate approaches; the first approach is based on double thresholds voice activity detection and improved Mel‐frequency cepstral coefficients (MFCC), while the second approach is based on discrete wavelet transform along with modified MFCC algorithm. Utilizing the best values for all parameters in just mentioned techniques, our proposed system achieved a recognition rate of 98.7% using the first approach, and 98.86% using the second approach of which is better in ratio than the first one but slower in processing which is a critical point for a real time system. Both proposed approaches were compared with other relevant approaches and their recognition rates were noticeably higher.

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A New Step in Arabic Speech Identification: Spoken Digit Recognition

Cited by 7 publications

References 1 publication

A View-Based T¿eplitz-Matrix-Supported System for Word Recognition without Segmentation

A View-Based T¿eplitz-Matrix-Supported System for Word Recognition without Segmentation

Wavelet LPC with Neural Network for Spoken Arabic Digits Recognition System

An efficient speech recognition system for arm‐disabled students based on isolated words

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