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Link to original content: https://doi.org/10.48550/arXiv.2302.09331
[2302.09331] Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition
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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2302.09331 (eess)
[Submitted on 18 Feb 2023]

Title:Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition

Authors:Xie Chen, Ziyang Ma, Changli Tang, Yujin Wang, Zhisheng Zheng
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Abstract:Recent years have witnessed a boom in self-supervised learning (SSL) in various areas including speech processing. Speech based SSL models present promising performance in a range of speech related tasks. However, the training of SSL models is computationally expensive and a common practice is to fine-tune a released SSL model on the specific task. It is essential to use consistent front-end input during pre-training and fine-tuning. This consistency may introduce potential issues when the optimal front-end is not the same as that used in pre-training. In this paper, we propose a simple but effective front-end adapter to address this front-end discrepancy. By minimizing the distance between the outputs of different front-ends, the filterbank feature (Fbank) can be compatible with SSL models which are pre-trained with waveform. The experiment results demonstrate the effectiveness of our proposed front-end adapter on several popular SSL models for the speech recognition task.
Subjects: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
Cite as: arXiv:2302.09331 [eess.AS]
  (or arXiv:2302.09331v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2302.09331

Submission history

From: Xie Chen [view email]
[v1] Sat, 18 Feb 2023 13:46:12 UTC (1,553 KB)
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