Real-time spoken Arabic digit recognizer

Abdulla, Waleed H.; Abdul-Karim, M. A. H.

doi:10.1080/00207218508920741

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…2, is a distinct feature point because it is the location where solid intensity diffusion occurs in more than one direction, and it is the also point where the frequency of the gradient change becomes maximum [12]. The operator for detecting a corner point should be well confined at its correct position, repeatable, and efficient as well as have the ability to avoid spurious detection [13]. The corners of a polygonal object plane, e.g., roof and wall, can be determined in an image by detecting and intersecting edge straight lines bounding the plane.…”

Section: Fig 1 Boundary Detectionmentioning

confidence: 99%

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Zakariah¹,

Al-Shalfan²

2020

IJCEE

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Section: Fig 1 Boundary Detectionmentioning

confidence: 99%

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Zakariah¹,

Al-Shalfan²

2020

IJCEE

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“…While Arabic has not been the object of as much linguistic research as to other languages such as English and Japanese, some researcher has been conducted on Arabic digit recognition. In 1985, Hagos [6] and Abdullah [7] separately reported Arabic digit recognizers. Hagos designed a speaker-independent Arabic digit recognition system that used template matching for input utterances.…”

Section: Spoken Digits Recognitionmentioning

confidence: 99%

Investigating spoken Arabic digits in speech recognition setting

Alotaibi

2005

Information Sciences

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“…Some research has been conducted on Arabic digit recognition. In 1985, Hagos [6] and Abdullah [7] separately reported Arabic digit recognizers. Hagos designed a speaker-independent Arabic digit recognizer that used template matching for input utterances.…”

Section: Spoken Digits Recognitionmentioning

confidence: 99%

A Simple Time Alignment Algorithm for Spoken Arabic Digit Recognition

Alotaibi

2009

eng

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The problem associated with spectral sequence comparison for speech comes from the fact that different acoustic renditions, or tokens, of the same speech utterance are seldom realized at the same speed across the entire utterance. In this paper a simple and effective time alignment was introduced for spoken Arabic digit recognition systems. We meant with simplicity here not only in its need for low computational power, but also simplicity to understand, to implement, and to explain to others. While high power computers are available today, time alignment algorithms, such as dynamic time warping algorithm and hidden Markov models need relatively high CPU time, which should be reserved for other complicated tasks. This algorithm has a high accuracy rate considering the very limited number of frames taken from input utterances to be used in training or testing. An artificial neural network based speech recognition system was designed and tested with automatic Arabic digit recognition to test this time alignment algorithm. The system is an isolated whole word speech recognizer and it was implemented in a multi-speaker mode (i.e., the same set of speakers was used in both the training and testing phases). During recognition process, digitized speech was cleaned of noise, then the signal was pre-emphasized and it was windowed and blocked by Hamming window, the time alignment algorithm was used to compensate for the differences in the utterance length and misalignments between phonemes. Frames features were extracted using MFCC coefficients to reduce the amount of the information in the input signal. Finally, the neural network classified the unknown digit. This recognition system achieved 99.48% correct digit recognition while using only seven frames in the time alignment algorithm.

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Real-time spoken Arabic digit recognizer

Cited by 12 publications

References 6 publications

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Investigating spoken Arabic digits in speech recognition setting

A Simple Time Alignment Algorithm for Spoken Arabic Digit Recognition

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