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Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced Languages

Domain:

natural language processing

Record type:

paper
Creator:
EbeZes
Host:avatar
In this paper, we investigate the effect of layer freezing on the effectiveness of model transfer in the area of automatic speech recognition. We experiment with Mozilla's DeepSpeech architecture on German and Swiss German speech datasets and compare the results of either training from scratch vs. transferring a pre-trained model. We compare different layer freezing schemes and find that even freezing only one layer already significantly improves results. Published at KONVENS 2021

Visit

arxiv.org

Tasks

automatic speech recognitionspeech processingtransfer learning

Tags

Computation and LanguageI.2.7

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