Exploring Innovations in Speech Recognition: An in-depth Analysis and Real-world Applications

Speech recognition, a field with a rich history, enables computers to accurately transcribe spoken words captured via a microphone, facilitating human-computer interaction. In the last ten years, speech recognition has become much better by using deep neural networks. These networks replaced old methods and improved how accurately computers understand speech. We have used DeepSpeech2 model for speech recognition and added some changes which helps computers understand different kinds of speech, like accents and noisy backgrounds. Deep learning has played a big role in making this happen. It helps computers learn from sound directly and makes them better at understanding speech. Overall, these improvements mean that computers can now understand speech much better, no matter how it sounds. Recurrent Neural Networks, Convolutional Neural Networks had also played crucial roles in this advancement, learning patterns from sequential data, and enhancing system robustness. These innovations have led to more accurate and adaptable speech-to-text systems, fostering broader applications, and meeting diverse user needs and convert into more simpler words which helped us to achieve error rate of 17 percent.