  Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to
achieve high accuracy and fast inference by simplifying the autoregressive (AR)
generation process of conventional models. Connectionist temporal
classification (CTC) is one of the key techniques used in NAR ASR models. In
this paper, we propose a new model combining CTC and a latent variable model,
which is one of the state-of-the-art models in the neural machine translation
research field. A new neural network architecture and formulation specialized
for ASR application are introduced. In the proposed model, CTC alignment is
assumed to be dependent on the latent variables that are expected to capture
dependencies between tokens. Experimental results on a 100 hours subset of
Librispeech corpus showed the best recognition accuracy among CTC-based NAR
models. On the TED-LIUM2 corpus, the best recognition accuracy is achieved
including AR E2E models with faster inference speed.
