Al-Noor Journal for Information Technology and Cybersecurity

The rapid advancement of self-supervised learning (SSL) techniques has significantly impacted the field of speech recognition, enabling models to leverage vast amounts of unlabelled data. This review provides an in-depth analysis of various SSL methodologies, comparing their effectiveness and applicability in speech recognition tasks. By examining key studies and their findings, we aim to highlight the strengths and limitations of different approaches, offering insights into future research directions.

With an emphasis on deep learning and bio-inspired optimization techniques, this paper provides an extensive overview of current developments in voice and emotion detection systems. Advanced recurrent networks like GRU and SVNN, attention-based encoder-decoder frameworks, and hybrid CNN-LSTM architectures are just a few of the models examined in the examined papers. In order to increase robustness, feature extraction methods like MFCC, PLPC, LPCC, and log Mel-filter banks are frequently used in conjunction with data augmentation techniques including speed perturbation, noise injection, and pitch shifting. To enhance feature selection and classifier performance, a number of optimization methods are used, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Glowworm Swarm Optimization (GSO), and innovative hybrids like MUPW and GREO. The examined works show state-of-the-art accuracy in a variety of tasks, such as multimodal (audio-visual) recognition systems, Arabic dialect recognition, and emotional speech classification. According to experimental results, there are significant improvements in performance compared to standard models; in certain systems, accuracy rates can approach 99.76%. The increasing efficacy of combining deep learning with intelligent optimization is highlighted in this paper, which also makes recommendations for future developments including transducer-based architectures, real-time adaptation, and domain-specific data augmentation.

With an emphasis on deep learning and bio-inspired optimization techniques, this paper provides an extensive overview of current developments in voice and emotion detection systems. Advanced recurrent networks like GRU and SVNN, attention-based encoder-decoder frameworks, and hybrid CNN-LSTM architectures are just a few of the models examined in the examined papers. In order to increase robustness, feature extraction methods like MFCC, PLPC, LPCC, and log Mel-filter banks are frequently used in conjunction with data augmentation techniques including speed perturbation, noise injection, and pitch shifting. To enhance feature selection and classifier performance, a number of optimization methods are used, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Glowworm Swarm Optimization (GSO), and innovative hybrids like MUPW and GREO. The examined works show state-of-the-art accuracy in a variety of tasks, such as multimodal (audio-visual) recognition systems, Arabic dialect recognition, and emotional speech classification. According to experimental results, there are significant improvements in performance compared to standard models; in certain systems, accuracy rates can approach 99.76%. The increasing efficacy of combining deep learning with intelligent optimization is highlighted in this paper, which also makes recommendations for future developments including transducer-based architectures, real-time adaptation, and domain-specific data augmentation.

This in-depth review looks at the most recent developments in high-performance models for Natural Language Processing (NLP), with a focus on transformer-based architectures and large language models (LLMs), which have changed the field. The rapid growth of model capabilities has changed the way machines understand, generate, and use human language, opening up new possibilities and problems in many areas. The review talks about important research trends, such as new ways to build transformer models, rules for scaling up performance, ways to make systems more efficient, how to make them work in more than one language, how to test them, how to think about ethics, how to protect them from attacks, how to explain them, and how to distil knowledge. Even though there has been a lot of progress, there are still big problems, such as needing a lot of computing power, ethical issues with bias and safety, not being able to understand things easily, and having trouble evaluating things. The review provides publications a structured overview of the current state of affairs, pointing out promising research directions and practical issues to think about when using high-performance NLP models. The results show how these technologies could change the world, but they also stress the need for responsible development that takes into account technical limitations and social effects.

Automated detection of human emotion from speech signals is a relatively new area in artificial intelligence aimed at determining the emotions people express through their speech. Traditionally, SER did feature extraction recognition with handcrafted ones and classical machine learning ones such as SVM (support vector machines) and HMM (hidden Markov models). The richness of emotions made these methodologies however challenging. The evolution of deep learning, in particular CNNs, RNNs, and other Transformer-based structures, has greatly improved the accuracy and robustness of SER systems. In this work, the SER is studied in depth taking into account the most relevant methods and feature extraction methods as well as an introduction of benchmark databases. It also includes augmentation methods, evaluation measures and the difficulties of real-time processing. Regardless of the advancements, SER continues to encounter challenges, including scarcity of datasets, imbalance between classes, domain adaptation, and high computational requirements. The review highlights unanswered questions regarding research and analyses. future directions, including multimodal fusion, self-supervised learning, and Explainable AI.