Research on Small Sample Language Speech Recognition Based on Transfer Learning
The paper proposes a transfer learning approach for small sample language speech recognition and investigates its implementation and effectiveness.In order to overcoming challenges such as insufficient data samples,low data quality,and the absence of suitable dictionaries in small sample language speech recognition,the research is grounded in the principles of transfer learning algorithms and introduces a method involving specialized dialectal processing of Mandarin pronunciation dictionaries and text corpora.The approach follows an iterative training process,which results in the creation of unique text corpora tailored specifically to Southwest Mandarin from a linguistic perspective.The language model demonstrates a significant improvement in prediction accuracy.The results of comparative experiments reveal that the transfer learning model performs well in terms of character error rates on both Mandarin and Southwest Mandarin datasets.Ultimately,the character error rate for Southwestern Mandarin speech recognition results falls below 14.4%,reaching 5.50%on the AISHELL-1 Mandarin public dataset.This accomplishment stood as the best recognition result among models of the same period,showcasing the successful transfer of knowledge from Mandarin to Southwest Mandarin.
万方数据
维普
