Syllable-based automatic arabic speech recognition in noisy enviroment

Azmi, M.M.; Tolba, Hesham

doi:10.1109/icalip.2008.4590209

Cited by 12 publications

(11 citation statements)

References 18 publications

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“…Experiments in [3] demonstrated that syllables are less sensitive to degradations of the speech signal due to interfering noise or distortions. In a previous work, we showed that the syllablebased ASR rate outperformed the ASR rate obtained using monophones, triphones and words by 2.68%, 1.19% & 1.79% respectively [4]. In this paper, we evaluate the use of syllables for the robustness of the automatic recognition of dysarthric speech.…”

Section: Introductionmentioning

confidence: 92%

Comparative Experiments to Evaluate the Use of Syllables for the Improvement of Automatic Recognition of Dysarthric Speech

Tolba

2009

2009 16th International Conference on Systems, Signals and Image Processing

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In this paper, we propose to use syllables as the acoustic units representing speech signals in an automatic speech recognition (ASR) system in order to improve the performance of the automatic recognition of dysarthric speech. The motivation behind using syllables comes from studies of human perception which demonstrate the central role of the syllable played in human perception and generation of speech. To test our proposed approach, a syllable-based speaker-independent HMM-based ASR system was designed using Hidden Markov Model Toolkit (HTK). A series of experiments on dysarthric speech has been carried out using a subset of NEMOURS database. The obtained results show that the relative improvement in the recognition rate using syllables were found to be 8.18% and 15.48% compared to the recognition rates obtained using monophones and triphones, respectively.

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Section: Introductionmentioning

confidence: 92%

Comparative Experiments to Evaluate the Use of Syllables for the Improvement of Automatic Recognition of Dysarthric Speech

Tolba

2009

2009 16th International Conference on Systems, Signals and Image Processing

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“…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 [13], 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: Related Workmentioning

confidence: 99%

“…In particular, a DCT is a Fourierrelated transform which is basically similar to the discrete Fourier transform (DFT), but using only real numbers [66,71]. As being formulated in (13), DCT is the preferred technique to use, when planning to get back to the time domain, as of having a highly uncorrelated feature. Actually, logarithm condenses a dynamic range of values whereas the minor differences at high amplitudes do not affect the humans much when compared to that at low amplitudes.…”

Section: Step 4 : Mel Filter Bankmentioning

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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“…Many HMMbased ASR systems for continuous Arabic speech have reached various levels of recognition accuracy and encouraging performances which have been achieved [14][15][16][17][18]. The accuracy of recognition is usually measured by the correct percentage of recognized phonemes.…”

Section: Research Efforts Summarymentioning

confidence: 99%

Helpful Statistics in Recognizing Basic Arabic Phonemes

Khelifa¹,

Yousfi²,

Elhadj³

et al. 2017

ijacsa

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Abstract-The recognition of continuous speech is one of the main challenges in the building of automatic speech recognition (ASR) systems, especially when it comes to phonetically complex languages such as Arabic. An ASR system seems to be actually in a blocked alley. Nearly all solutions follow the same general model. The previous research focused on enhancing its performance by incorporating supplementary features. This paper is part of ongoing research efforts aimed at developing a high-performance Arabic speech recognition system for learning and teaching purposes. It investigates a statistical analysis of certain distinctive features of the basic Arabic phonemes which seems helpful in enhancing the performance of a baseline HMMbased ASR system. The statistics are collected using a particular Arabic speech database, which involves ten different male speakers and more than eight hours of speech which covers all Arabic phonemes. In HMM modeling framework, the statistics provided are helpful in establishing the appropriate number of HMM states for each phoneme and they can also be utilized as an initial condition for the EM estimation procedure, which generally, accelerates the estimation process and, thus, improves the performance of the system. The obtained findings are presented and possible applications of automatic speech recognition and speaker identification systems are also suggested.

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Syllable-based automatic arabic speech recognition in noisy enviroment

Cited by 12 publications

References 18 publications

Comparative Experiments to Evaluate the Use of Syllables for the Improvement of Automatic Recognition of Dysarthric Speech

Comparative Experiments to Evaluate the Use of Syllables for the Improvement of Automatic Recognition of Dysarthric Speech

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

Helpful Statistics in Recognizing Basic Arabic Phonemes

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