## 2 Literature Review  
In document [5], the objective of automatic speech recognition is to give computers the best possible ability to recognize and understand human speech. Speech recognition models can be implemented using a variety of methods. Neural networks with deep learning are used to support one of the newest methods for speech recognition. Arabic receives the least attention in terms of speech recognition technology, despite being the language that is spoken the most. Paper [6] provides an overview of the research on Arabic voice recognition. It also provides some insight into the resources and toolkits available for the development of Arabic voice recognition systems. Many products that effectively use automatic speech recognition to facilitate human-machine communication have been developed. Applications for speech recognition perform worse when there is minimal background noise or reverberation [7]. Both text and audio transcriptions are used for the entire process of training automatic speech recognition (ASR) neural network systems. This work assesses the representation quality in a variety of classification tasks by comparing phonemes and graphemes in addition to various articulatory properties. This article shows how consistently different features are represented across deep neural network systems by analyzing three datasets in two languages: Arabic and English [8]. Researchers sought to develop highly effective recognizers for two drastically dissimilar languages, like English and Mandarin. In this paper, researchers investigated several network topologies and discovered some useful methods, like batch normalization and sort grad to improve numerical optimization and look ahead convolution for unidirectional models. This research was made possible by a highly effective training method [9]. The ability of the Deep Speech network to identify distinct Bengali speech samples is examined in their literature. This network models internal phoneme representation using recurrent LSTM layers as its foundation. Convolutional layers are added at the bottom, eliminating the need to make any assumptions regarding internal phoneme alignment. A connectionist temporal classification (CTC) damage task was used to train the model, and a beam search decoder was used to generate the transcript. The developed method yielded a lower word error rate and a lower character error rate on the Bengali real number speech dataset [10]. An innovative end-to-end speech acknowledgment technique is provided by reference [11], which makes use of a hybrid CTC-attention archetype inside the multitask learning context to increase convergence and resilience and lessen alignment problems. Its superiority over the CTC and attention-based encoder-decoder baselines is demonstrated in a trial using the WSJ and CHiME-4 tasks, yielding 5.4-14.6% qualified enhancements in character error rate (CER). For speech-to-text, work [12] uses a shared task on SwissText/KONVENS. End-to-end training of a neural network is based on Mozilla Deep Speech. Postprocessing, data augmentation, and transfer learning from standard German and English were applied. The system produces a WER of 58.9%. One of natural language processing's (NLP) most crucial tasks is next-word prediction, which is also called language modeling. A recurrent neural network (RNN) model is being developed with TensorFlow to predict the top 10 words from a 40-letter text provided by a client. The objective is to accurately and quickly predict ten or more words [13]. Study [14] offers a novel approach that can be used for a variety of NLG tasks to predict the next word in a Hindi sentence using Long Short-Term Memory (LSTM) and Bi-LSTM deep learning techniques. The approach achieves 59.46% and 81.07% accuracy, respectively. Study [15] explores the use of sub-word units (character and factored morphological decomposition) in neural language modeling. Using Czech as a case study, it was found that character-based embedding significantly improved model performance, even with unstable training.