Syllable-based automatic Arabic speech recognition in different conditions of noise

Azmi, M.M.; Tolba, Hesham

doi:10.1109/icosp.2008.4697204

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…The motivation behind using syllables as a unit of training is that the pronunciation variation and co-articulation effects can be captured directly and train the acoustic model accordingly. The study has also been motivated by other studies, such as Azmi (2008), which used syllables as units in developing ASR systems and reported promising results. Another source of motivation are some studies that focused on the human perception of speech such as Räsänen (2015).…”

Section: Including a Stress Marker For Every Stressed Syllable In A Syllable-based Modelmentioning

confidence: 99%

Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

Alsharhan¹,

Alnajem²

2021

SJKFU

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Developing natural language processing applications for Arabic must consider the different linguistic characteristics found in speech and translate those characteristics to script in order to reduce computational complexity and therefore reduce the word error rate (WER). Suprasegmental features are fundamental properties of speech that can enhance the performance of many natural speech processing applications. The present study considered stress as a prosodic feature comprising the prominence of syllables in speech by developing a tool that generated phonetic transcriptions and predicted the stress position. The generated transcription was used to create the phonetic dictionary necessary for developing an automatic speech recognition (ASR) system. This tool had to be accurate, linguistically motivated, and applicationally useful; therefore, the effectiveness of the generated stress-marked phonetic dictionary was tested by comparing the performance of a standard fixed dictionary-based system with that of one using the automatically generated dictionary. The research reported a 5.6% reduction in WER when using a dictionary with stress markers attached to each phone in the stressed syllable and a 3.5% reduction in WER when using a dictionary with stress markers assigned only to stressed vowels. These results encourage future studies to employ prosodic features of speech when developing different speech processing applications.

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Section: Including a Stress Marker For Every Stressed Syllable In A Syllable-based Modelmentioning

confidence: 99%

Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

Alsharhan¹,

Alnajem²

2021

SJKFU

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“…were developed for diverse tasks such as by Hyassat and Speech processing techniques Elshafei (1991) and Zitar (2006), Azmi and Tolba (2008), Choubassi et al natural language (Farghaly and Shaalan, 2009; and Nofal et al (2004). Elshafei et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Large Vocabulary Arabic Continuous Speech Recognition using Tied States Acoustic Models

Azim¹,

Asayesh²,

Badr³

et al. 2019

Asian J. Inf. Technol.

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The Hidden Markov Model (HMM) lies at the heart of the modern speech recognition systems as it provides a simple, effective and straight forward frame work to model the time varying acoustic features of the speech signals. The basic process of building HMM based speech recognition systems is a straight forward process. Nevertheless, the proper parameter estimation of such models requires large training data. Therefore, parameter tying techniques were developed to reduce the parameters of HMMs without affecting the overall system performance. This study proposes an Arabic phonetic decision tree necessary to build Tied State tri-phone HMMs. Experimental results show promising word correctness when compared with both data driven tri-phone models and phoneme based models. The maximum word correctness achieved by the proposed approach was 95.13%. Whereas it reached 78.03 and 58.45% using data driven tri-phones and phoneme based HMMs, respectively, when tested on the same benchmark database.

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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%

“…On the other hand, Automatic Speech Recognition (ASR) is a technology that allows a computer to identify the words that a person speaks into a microphone or telephone with a wide range of applications. Based on that, investigations were made, in reference , between monophone, triphone, syllable, and word‐based calculations for recognizing Egyptian Arabic digits. Actually, 39 MFCCs were extracted as features for each recorded voice in the database.…”

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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Syllable-based automatic Arabic speech recognition in different conditions of noise

Cited by 8 publications

References 4 publications

Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

Large Vocabulary Arabic Continuous Speech Recognition using Tied States Acoustic Models

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

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