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A Weight Moving Average Based Alternate Decoupled Learning Algorithm for Long-Tailed Language Identification
2021
Conference of the International Speech Communication Association
Language identification (LID) research has made tremendous progress in recent years, especially with the introduction of deep learning techniques. However, for real-world applications where the distribution of different language data is highly imbalanced, the performance of existing LID systems is still far from satisfactory. This raises the challenge of long-tailed LID. In this paper, we propose an effective weight moving average (WMA) based alternate decoupled learning algorithm, termed
doi:10.21437/interspeech.2021-776
dblp:conf/interspeech/WangLSF0021
fatcat:6r6uxlsiyjeb5o3hvv3qaouaiq