Advancements in Speech Recognition Using Artificial Intelligence: A Comprehensive Review and Future Directions

Abstract

Speech recognition technology has significantly evolved with the integration of Artificial Intelligence (AI), enabling machines to interpret and respond to human speech with increasing accuracy and fluency. This paper provides a comprehensive overview of AI-driven approaches in speech recognition, focusing on deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformer-based architectures. The study highlights key advancements in natural language processing (NLP), acoustic modeling, and language modeling, along with datasets commonly used in training these models. Additionally, the paper discusses real-world applications such as virtual assistants, transcription services, and assistive technologies, while addressing ongoing challenges such as multilingual processing, background noise, and contextual understanding. Future directions emphasize the need for more robust, scalable, and privacy-conscious AI models to further advance the field.