Attention Based Encoder Decoder

Attention-based encoder-decoder models are a powerful class of neural networks used for sequence-to-sequence tasks, primarily aiming to improve the accuracy and efficiency of processing sequential data like speech and text. Current research focuses on enhancing these models through architectural innovations such as incorporating non-autoregressive decoding for faster inference, multimodal integration of acoustic and semantic information, and hybrid approaches combining different decoder architectures (e.g., CTC, attention, transducer). These advancements have significant implications across diverse fields, including speech recognition, machine translation, and medical image analysis, by enabling more accurate, efficient, and robust processing of complex sequential data.