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Transfer Learning based Nonnative Acoustic Modeling for Pronunciation Error Detection Richest Duan1, Satsuma Kawahara1, Mistake Dantsuji2, Hiroshi Nanjo2 1School of Informatics, Kyoto University,
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Transfer learning based non-native is a machine learning technique where a model developed for a task is reused as the starting point for a model on a new task.
Researchers, data scientists, or developers who are working on developing models using transfer learning techniques are required to file transfer learning based non-native.
To fill out transfer learning based non-native, one must document the specific transfer learning approach used, any pre-trained models utilized, and the performance metrics achieved.
The purpose of transfer learning based non-native is to leverage existing knowledge from pre-trained models to improve the performance of models on new tasks.
Information such as the dataset used, the specific transfer learning techniques applied, the architecture of the model, and the results obtained must be reported on transfer learning based non-native.
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