The CTC-based automatic speech recognition (ASR) models without the external language model usually lack the capacity to model conditional dependencies and textual interactions. In this paper, we present a Gated Interlayer Collaboration (GIC) mechanism to improve the performance of CTC-based models, which introduces textual information into the model and thus relaxes the conditional independence assumption of CTC-based models. Specifically, we consider the weighted sum of token embeddings as the textual representation for each position, where the position-specific weights are the softmax probability distribution constructed via inter-layer auxiliary CTC losses. The textual representations are then fused with acoustic features by developing a gate unit. Experiments on AISHELL-1, TEDLIUM2, and AIDATATANG corpora show that the proposed method outperforms several strong baselines.
@article{arxiv.2205.12462,
title = {Improving CTC-based ASR Models with Gated Interlayer Collaboration},
author = {Yuting Yang and Yuke Li and Binbin Du},
journal= {arXiv preprint arXiv:2205.12462},
year = {2023}
}