INTELLIGENT SPEECH RECOGNITION USING FRACTAL AMENDED GRASSHOPPER OPTIMIZATION ALGORITHM WITH DEEP LEARNING APPROACH

Reema G. Al-Anazi, Abdullah Saad Al-Dobaian, Asma Abbas Hassan, Manar Almanea, Ayman Ahmad Alghamdi, Somia A. Asklany, Hanan Al Sultan and Jihen Majdoubi
Additional contact information
Reema G. Al-Anazi: Department of Arabic Language and Literature, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
Abdullah Saad Al-Dobaian: ��Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Asma Abbas Hassan: ��Computer Science Department, Applied College at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
Manar Almanea: �Department of English, College of Languages and Translation, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Ayman Ahmad Alghamdi: �Department of Arabic Teaching, Arabic Language Institute, Umm Al-qura University, Saudi Arabia
Somia A. Asklany: ��Department of Computer Science and Information Technology, Faculty of Sciences and Arts, Turaif, Northern Border University, Arar 91431, Kingdom of Saudi Arabia
Hanan Al Sultan: *Department of English, College of Arts, King Faisal University, Saudi Arabia
Jihen Majdoubi: ��†Department of Computer Science, College of Science and Humanities at Alghat, Majmaah University, Al-Majmaah 11952, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-12

Abstract: Humans prefer to convey information through speech utilizing similar language. Speech detection is the capability to recognize the spoken words of the speaking person. Recent work demonstrates the increased attention among researcher workers in this field specially in Brain-Like computing applications and emphasizes the real-world usability of speech for speaker recognition across different applications. Automatic speech recognition (ASR) is the method of identifying human speech and converting it into text. This study has gained much popularity in recent times. It is a crucial area of research for human-to-machine interaction. Pioneer methods are concerned with manual feature extraction and classical algorithms including Hidden Markov Models (HMM), Gaussian Mixture Model (GMM), and the Dynamic Time Warping (DTW) model. In recent years, neural networks, namely convolutional neural networks (CNN), recurrent neural networks (RNN), and Transformers, have been utilized in the context of ASR and reached outstanding performance over the past few years. This study introduces Intelligent Speech Recognition using the Fractal Amended Grasshopper Optimization Algorithm with Deep Learning (ISR-AGODL) approach. The presented ISR-AGODL technique correctly identifies and recognizes speech signals. In the ISR-AGODL technique, the speech signals are transformed into spectrograms. Besides, the features are derived using the deep convolutional neural networks (DCNN) model. Followed by the Fractals AGO technique is utilized for the choosing of hyperparameters. Finally, the recognition of speech signals is achieved using the extreme gradient boosting (XGBoost) model. The simulation outcomes of the ISR-AGODL method can be validated using a benchmark dataset. The experimental results of the ISR-AGODL method portrayed a superior accuracy outcome of 96.34% over other models.

Keywords: Speech Recognition; Fractals Amended Grasshopper Optimization; Speech Signals; Deep Learning; Human-To-Machine Interaction; Brain-Like Computing Applications (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400298

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