Speech recognition in pipeline engineering domain based on transfer learning and knowledge distillation
Jingyi FENG , Xianqiang GUO , Cungen ZHANG , Yuangeng LYU , Leping LIU
Water Resources and Hydropower Engineering ›› 2025, Vol. 56 ›› Issue (S2) : 6 -9.
Municipal pipeline engineering is a key area in urban construction. Traditional method of recording expert construction guidance are inefficient. Speech recognition technology can improve efficiency but often has low accuracy in specialized domains. A speech recognition model was proposed for the pipeline engineering domain based on transfer learning and knowledge distillation. The model uses an end-to-end approach, adapts parameters from an open-domain model to the target domain via transfer learning, and then compresses the model using knowledge distillation.[Results]show that transfer learning reduces the word error rate by 6.2%, and knowledge distillation reduces model parameters by 83.2 MB while improving inference speed.
pipeline engineering / expert speech recognition / transfer learning / knowledge distillation / lightweight design
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