Robust Speech Recognition Techniques for Noisy Surroundings

In modern years, the accuracy and effectiveness of speech recognition (SR) is the hottest research area by far. It has been tough-going, playing an increasingly important role in many real-world applications. This method is being practiced in medicine for a several years as they are using it for sake of hearing aids where the performance and advancements has been drastically improved. This study provides a detailed analysis of diverse speech recognition test included two test contents and two auditory environments: quiet and noise. This research paper focuses on analyzing and improving speech recognition (SR) systems, particularly in noisy environments. It reviews traditional methods like noise reduction and adaptive filtering, alongside deep learning techniques such as convolutional and recurrent neural networks. The study examines the performance of SR systems in various scenarios, including quiet and noisy settings, and explores real-world applications in fields like transportation, offices, and hearing aids. Key challenges, such as handling noise and optimizing signal modelling, are highlighted, with experiments showing a 30% error reduction compared to state-of-the-art systems.