Convolutional Neural Network-Based Optimization of Automatic Speech Recognition for Noisy Environments

Speech recognition technology is a fascinating field that enables machines to comprehend and interpret human speech. It allows users to interact with computers, smartphones, and other devices, using spoken commands rather than traditional input methods, like typing. Speech recognition systems analyse audio input, typically in the form of spoken words or phrases, and convert them into text or commands that computers can understand. The journey of speech recognition technology has been remarkable, evolving from simple command-based systems to advanced natural language processing algorithms capable of understanding context, accents, and even emotions. While speech recognition has made significant strides, challenges persist, particularly in accurately handling noisy environments and distinguishing between similar-sounding words. This study aimed at developing an optimal automatic speech recognition system under a noisy environment, using machine learning techniques. In addition, the study aimed at evaluating the performance of the developed system. Our approach used a multi-stage preprocessing pipeline that was intended to reduce background noise while maintaining voice comprehensibility. Furthermore, the approach investigated several CNN architectures and hyperparameter combinations in order to determine the best settings for various kinds of noise conditions. The model was developed using Google Collab and TensorFlow, an open-source machine-learning platform. This model used a transformer-hugging face, which is a pre-trained model. Transformers deployed convolutional neural networks (CNN) that were trained with data collected by Facebook wac2 vec. Through empirical tests, we show that our proposed CNN-based ASR model performs significantly better than existing ASR systems, with notable gains in overall recognition accuracy and Word Error Rate across many benchmark datasets covering various noisy environments. The findings show that CNN integration not only improves speech recognition systems' resilience but also sets the stage for next studies on adaptive ASR technologies with practical applications. Our results highlight how deep learning techniques can transform ASR systems to attain dependable performance in the face of environmental challenges.