  In this paper, we present a new open source toolkit for speech recognition,
named CAT (CTC-CRF based ASR Toolkit). CAT inherits the data-efficiency of the
hybrid approach and the simplicity of the E2E approach, providing a
full-fledged implementation of CTC-CRFs and complete training and testing
scripts for a number of English and Chinese benchmarks. Experiments show CAT
obtains state-of-the-art results, which are comparable to the fine-tuned hybrid
models in Kaldi but with a much simpler training pipeline. Compared to existing
non-modularized E2E models, CAT performs better on limited-scale datasets,
demonstrating its data efficiency. Furthermore, we propose a new method called
contextualized soft forgetting, which enables CAT to do streaming ASR without
accuracy degradation. We hope CAT, especially the CTC-CRF based framework and
software, will be of broad interest to the community, and can be further
explored and improved.
