Editing Development of End-to-End Models (section)

==== Advantage of End-to-End Models ASR ====
Initially, end-to-end models simplify the ASR pipeline substantially by directly generating characters or even words. Conversely, the design of traditional hybrid models is intricate, demanding extensive expertise and years of ASR experience.

Furthermore, the utilization of a single network for ASR makes end-to-end models significantly more compact compared to traditional hybrid models. This compactness enables easy deployment of end-to-end models on high-accuracy devices.<ref>Li J .Recent Advances in End-to-End Automatic Speech Recognition[J].  2021.https://arxiv.org/abs/2111.01690</ref>

Last, end-to-end models has a much simpler training approach with models, which reduces learning time, decoding time, and allows joint optimization with subsequent processing, such as understanding the natural language.<ref>Orken M , Dina O , Keylan A ,et al.A study of transformer-based end-to-end speech recognition system for Kazakh language[J].Scientific Reports[2023-09-17].https://www.nature.com/articles/s41598-022-12260-y</ref>