The primary objective of the study is to determine Speech Recognition has become an essential technology in various application, including virtual assistants, healthcare, customer service, and accessibility tools. This paper presents an AI-driven approach using Deep Learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), for accurate speech recognition. The proposed model processes audio signals, extracts features, and converts speech into text with high accuracy. We evaluate the model on benchmark datasets and compare it with traditional speech recognition techniques. The results demonstrate improved performance, making AI - Based speech recognition a viable solution for real - world applications. This significance of speech recognition AI and human computer interaction. The major challenges such as background noise, speaker variability. There have been accuracy issues with traditional methods based on Gaussian mixture models (GMMs) and hidden Markov models (HMMs), especially in noisy settings. There are also persistent challenges with regard to privacy, data security, and the underrepresentation of low-resource languages. Achieving greater noise robustness, extending language inclusivity, boosting contextual and emotional comprehension, and strengthening multilingual support are key to the future of AI-based speech recognition. To improve the accuracy, morality, and accessibility of voice recognition, additional research and innovation are needed.
Deep learning, NLP, CNN, Audio Processing, Real-time transcription
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