E2e Automatic Speech Recognition

End-to-end (E2E) automatic speech recognition (ASR) aims to directly transcribe speech to text without intermediate steps like phoneme recognition, improving efficiency and potentially accuracy. Current research focuses on enhancing robustness and adaptability across diverse speakers and domains, exploring architectures like connectionist temporal classification (CTC), attention-based models, recurrent neural network transducers (RNN-T), and mask-predict models, often combining them for improved performance. These advancements are significant because they address limitations of traditional ASR systems, leading to more accurate and versatile speech recognition in various applications, including low-resource languages and code-switching scenarios.