# Fast-U2++: Fast and Accurate End-to-End Speech Recognition in Joint CTC/Attention Frames

arXiv: https://arxiv.org/abs/2211.00941 · HTML: https://mlsanity.com/paper/2211.00941/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding](https://mlsanity.com/paper/2305.12425/) (2023)
- [Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer](https://mlsanity.com/paper/2308.16415/) (2023)
- [Delayed-KD: Delayed Knowledge Distillation based CTC for Low-Latency Streaming ASR](https://mlsanity.com/paper/2505.22069/) (2025)
- [Enhancing the Unified Streaming and Non-streaming Model with Contrastive Learning](https://mlsanity.com/paper/2306.00755/) (2023)

## Abstract (arXiv)

Recently, the unified streaming and non-streaming two-pass (U2/U2++) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy and latency. In this paper, we present fast-U2++, an enhanced version of U2++ to further reduce partial latency. The core idea of fast-U2++ is to output partial results of the bottom layers in its encoder with a small chunk, while using a large chunk in the top layers of its encoder to compensate the performance degradation caused by the small chunk. Moreover, we use knowledge distillation method to reduce the token emission latency. We present extensive experiments on Aishell-1 dataset. Experiments and ablation studies show that compared to U2++, fast-U2++ reduces model latency from 320ms to 80ms, and achieves a character error rate (CER) of 5.06% with a streaming setup.