Improving English Speech Recognition System Accuracy Using Machine Learning

This research significantly advanced English speech recognition technology by leveraging advanced machine learning techniques. The study developed a novel hybrid model combining deep learning, transfer learning, and reinforcement learning, which markedly improved recognition accuracy in complex environments. A diverse 1000-hour dataset was used for training, encompassing various accents and environmental conditions. The model's architecture integrated LSTM, CNN, and attention mechanisms, coupled with innovative feature extraction methods. Results showed substantial improvements across multiple metrics. The overall word error rate was reduced to 11.25%, outperforming existing benchmark models. Notably, the system achieved 86% accuracy in noisy environments and 92% in accent adaptation. For rapid speech, error rates decreased from 20% to 12%. These advancements expand the technology's applicability in fields such as healthcare, education, and customer service, paving the way for more robust and versatile speech recognition systems in real-world applications.