End 2 End Automatic Speech

End-to-end (E2E) automatic speech recognition (ASR) aims to directly transcribe speech to text using neural networks, eliminating the need for traditional, modular systems. Current research focuses on improving the robustness and efficiency of E2E ASR, particularly addressing challenges like handling rare words, improving confidence estimation, and adapting models to different domains and languages. This involves exploring various architectures, including Transformers, RNN-Transducers, and Conformers, along with techniques like data augmentation, pre-trained models, and discriminative training criteria such as Lattice-Free MMI. The resulting advancements have significant implications for improving the accuracy and efficiency of speech-to-text applications across diverse languages and noisy environments.