# CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency

arXiv: https://arxiv.org/abs/2005.13326 · HTML: https://mlsanity.com/paper/2005.13326/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 13 of 13)

- [Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision](https://mlsanity.com/paper/2406.02166/) (2024)
- [Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings](https://mlsanity.com/paper/2107.05038/) (2021)
- [CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR](https://mlsanity.com/paper/2203.16758/) (2022)
- [Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers](https://mlsanity.com/paper/2107.03007/) (2021)
- [Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation](https://mlsanity.com/paper/2306.01296/) (2023)
- [LLM-based phoneme-to-grapheme for phoneme-based speech recognition](https://mlsanity.com/paper/2506.04711/) (2025)
- [Pronunciation-Lexicon Free Training for Phoneme-based Crosslingual ASR via Joint Stochastic Approximation](https://mlsanity.com/paper/2507.06249/) (2025)
- [Deformable TDNN with adaptive receptive fields for speech recognition](https://mlsanity.com/paper/2104.14791/) (2021)
- [Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study](https://mlsanity.com/paper/2203.16757/) (2022)
- [SAN: a robust end-to-end ASR model architecture](https://mlsanity.com/paper/2210.15285/) (2022)
- [Low-Resourced Speech Recognition for Iu Mien Language via Weakly-Supervised Phoneme-based Multilingual Pre-training](https://mlsanity.com/paper/2407.13292/) (2024)
- [How I Built ASR for Endangered Languages with a Spoken Dictionary](https://mlsanity.com/paper/2510.04832/) (2025)
- [A Lexical-aware Non-autoregressive Transformer-based ASR Model](https://mlsanity.com/paper/2305.10839/) (2023)

## Abstract (arXiv)

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.