Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

Ibrahim, Ahmed B.; Seddiq, Yasser Mohammad; Meftah, Ali H.; Alghamdi, Mohammed H.; Selouani, Sid‐Ahmed; Qamhan, Mustafa A.; Alotaibi, Yousef Ajami; Alshebeili, Saleh A.

doi:10.1109/access.2020.3034762

Cited by 17 publications

(15 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is not only an effective and convenient way of information exchange, but also an important tool for humans to use machines. Whether it is the language communication between humans and machines, the noise processing of Chinese-English parallel corpus, especially the digital processing of voice signals, has a particularly important role [ 6 ]. Once voice recognition and voice synthesis technology are combined, people can leave the keyboard, receive voice commands, and perform operations [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

Tuo

Kong

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

Tuo

Kong

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Phonemes include all the distinct units of sound spoken in a language. The Arabic language consists of 34 phonemes that can be broken down into 28 consonants, three short, and three long vowels [4] known by their common names of Fatha, Damma, and Khasra. Pronunciation is a general term that includes several distinct features present in human languages.…”

Section: Arabic Phonemes and Their Pronunciationmentioning

confidence: 99%

An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning

et al. 2021

View full text Add to dashboard Cite

A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing.

show abstract

“…In conclusion, the sensitivity analysis method can be used to screen the updating parameters from the numerous parameters of the MGDM, thereby providing a foundation for the subsequent model updating. To improve the calculation efficiency, the KRG surrogate model was combined with the genetic algorithm [27]- [29] to determine parameter values that minimize the error between the simulation and experimental results within the value range of the updating parameters to improve the accuracy of the MGDM.…”

Section: B Updating Parameter Screening Based On Sobol Sensitivity Analysismentioning

confidence: 99%

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Qin

Liu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Model updating can improve the accuracy of the multistage gearbox dynamic model (MGDM). However, owing to the time-consuming calculations and numerous parameters of the MGDM, previous updating methods are not suitable for updating the MGDM. To solve the above problems, an improved dynamic model updating method that is suitable for updating the MGDM is proposed in this paper. Compared with previous methods, this updating method presents two improvements: (1) In the iterative calculation process, the surrogate model is used to replace the time-consuming MGDM, thereby solving the problem of low computational efficiency; (2) before the updating calculation, Sobol sensitivity analysis is performed to screen out updating parameters from numerous parameters. This study shows that the accuracy of the updated MGDM improved by 18.52% compared with the initial model, thereby enabling a more accurate prediction of the dynamic characteristics of the actual system. This proves the effectiveness of the improved updating method.INDEX TERMS Gear, gearbox, dynamic model, updating method, sensitivity analysis, surrogate model.

show abstract

Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

Cited by 17 publications

References 40 publications

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning

An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Contact Info

Product

Resources

About