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

• URL: http://arxiv.org/abs/2302.09331v1
• Date: Sat, 18 Feb 2023 13:46:12 GMT
• 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
• Abstract summary: Speech based SSL models present promising performance in a range of speech related tasks. It is essential to use consistent front-end input during pre-training and fine-tuning. We propose a simple but effective front-end adapter to address this front-end discrepancy.
• Score: 6.238268985570237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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