An Improved Wav2Vec 2.0 Pre-Training Approach Using Enhanced Local Dependency Modeling for Speech Recognition

wav2vec 2.0 is a recently proposed self-supervised pre-training framework for learning speech representation. It utilizes a transformer to learn global contextual representation, which is effective especially in low-resource scenarios. Besides, it was shown that combining convolution neural network and transformer to model both local and global dependencies is beneficial for e.g., automatic speech recognition (ASR), natural language processing (NLP). However, how to model the local and global dependence in pre-training models is still an open question in the speech domain. In this paper, we therefore propose a new transformer encoder for enhancing the local dependency by combining convolution and self-attention modules. The transformer encoder first parallels the convolution and self-attention modules, and then serialized with another convolution module, sandwiched by a pair of feed forward modules. Experimental results show that the pre-trained model using the proposed method can reduce the word error rate (WER) compared to the reproduced wav2vec 2.0 at the cost of slightly increasing the size of training parameters.