  In this paper, we present a new open source toolkit for automatic speech
recognition (ASR), named CAT (CRF-based ASR Toolkit). A key feature of CAT is
discriminative training in the framework of conditional random field (CRF),
particularly with connectionist temporal classification (CTC) inspired state
topology. CAT contains a full-fledged implementation of CTC-CRF and provides a
complete workflow for CRF-based end-to-end speech recognition. Evaluation
results on Chinese and English benchmarks such as Switchboard and Aishell show
that CAT obtains the state-of-the-art results among existing end-to-end models
with less parameters, and is competitive compared with the hybrid DNN-HMM
models. Towards flexibility, we show that i-vector based speaker-adapted
recognition and latency control mechanism can be explored easily and
effectively in CAT. We hope CAT, especially the CRF-based framework and
software, will be of broad interest to the community, and can be further
explored and improved.
