Delve deep into End-To-End Automatic Speech Recognition Models

Automatic Speech Recognition (ASR) has experienced significant advancements in recent years, with end-to-end approaches emerging as a promising paradigm shift. Unlike traditional ASR systems that rely on a pipeline of separate components, end-to-end models aim to directly transcribe speech inputs into text using deep learning architectures. In this paper, we conduct a comprehensive study on end-to-end ASR models. We review various architectures employed in end-to-end ASR, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. We explore different training methodologies, loss functions, and optimization algorithms used in end-to-end ASR. Additionally, we discuss large-scale datasets commonly used for training and evaluate the performance of end-to-end models using established evaluation metrics such as word error rate (WER). Furthermore, we analyze the strengths and weaknesses of end-to-end ASR models, highlight their applications in real-world scenarios, and discuss open challenges and future directions. By providing this comprehensive study, we aim to facilitate a deeper understanding of end-to-end ASR models and their potential for driving advancements in speech recognition technology.