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

arXiv 2302.09331 ↗First posted 18 Feb 2023eess.AS cs.CL cs.SDcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 18 Feb 2023.

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References · 15

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3 more
  1. 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 335
  2. 2021BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognitioncited by 88
  3. 2022MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targetscited by 8

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