CAT: A CTC-CRF Based ASR Toolkit Bridging the Hybrid and the End-to-End Approaches Towards Data Efficiency and Low Latency
Keyu An, Hongyu Xiang, Zhijian Ou
Abstract
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 nonmodularized 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.