  This paper proposes CTC-based non-autoregressive ASR with self-conditioned
folded encoders. The proposed method realizes non-autoregressive ASR with fewer
parameters by folding the conventional stack of encoders into only two blocks;
base encoders and folded encoders. The base encoders convert the input audio
features into a neural representation suitable for recognition. This is
followed by the folded encoders applied repeatedly for further refinement.
Applying the CTC loss to the outputs of all encoders enforces the consistency
of the input-output relationship. Thus, folded encoders learn to perform the
same operations as an encoder with deeper distinct layers. In experiments, we
investigate how to set the number of layers and the number of iterations for
the base and folded encoders. The results show that the proposed method
achieves a performance comparable to that of the conventional method using only
38% as many parameters. Furthermore, it outperforms the conventional method
when increasing the number of iterations.
