Before revision:  In this work, we propose an end-to-end model for the low-resource speech recognition, which fuses a pre-trained audio encoder (wav2vec2.0) and a pre-trained text decoder (BERT) . The two modules are connected by a linear attention mechanism without parameters.
After revision:  In this work, we propose an end-to-end model for the low-resource speech recognition, which fuses a pre-trained audio encoder (wav2vec2.0) and a pre-trained text decoder (BERT) into an end-to-end ASR model. The fused model only needs to learn the transfer from speech to language during fine-tuning on limited labeled data. The length of the two modalities is matched by a monotonic attention mechanism without parameters.