  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.
