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eess.AS2023★ 1 cited
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition
Dongji Gao, Hainan Xu, Desh Raj +3
Training automatic speech recognition (ASR) systems requires large amounts of well-curated paired data. However, human annotators usually perform "non-verbatim" transcription, whic…
eess.AS2023★ 2 cited
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Han Zhu, Dongji Gao, Gaofeng Cheng +3
When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, p…