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==== Significant Shift : Simplifying Speech Recognition ==== End-to-end modeling can directly translate the speech input into the output only using a single neural network, unlike the traditional one, which has several independent elements. In traditional ASR, the majority of ASR systems comprise distinct acoustic, pronunciation, and language model components, each trained separately. The creation of a pronunciation lexicon and the specification of phoneme sets for a specific language necessitate expertise and are time-intensive tasks. ([https://ibb.co/SQ4x7fF]) shows its structure. [https://ibb.co/5FGLdfG E2E] (Click here to view the end-to-end models workflow) speech recognition significantly simplifies the complexity of traditional models. Manual labeling of information is unnecessary, as the neural network can autonomously learn language and pronunciation details.<ref>Wang, Dong, Xiaodong Wang, and Shaohe Lv. 2019. βAn Overview of End-to-End Automatic Speech Recognition.β ''Symmetry'' 11(8):1018.https://www.mdpi.com/2073-8994/11/8/1018</ref>
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