# CUSIDE-T: Chunking, Simulating Future and Decoding for Transducer based Streaming ASR

arXiv: https://arxiv.org/abs/2407.10255 · HTML: https://mlsanity.com/paper/2407.10255/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [Delayed-KD: Delayed Knowledge Distillation based CTC for Low-Latency Streaming ASR](https://mlsanity.com/paper/2505.22069/) (2025)
- [SENS-ASR: Semantic Embedding injection in Neural-transducer for Streaming Automatic Speech Recognition](https://mlsanity.com/paper/2603.10005/) (2026)
- [X2Streaming-ASR: wait when uncertain, emit when ready for streaming ASR](https://mlsanity.com/paper/2609.08672/) (2026)

## Abstract (arXiv)

Streaming automatic speech recognition (ASR) is very important for many real-world ASR applications. However, a notable challenge for streaming ASR systems lies in balancing operational performance against latency constraint. Recently, a method of chunking, simulating future context and decoding, called CUSIDE, has been proposed for connectionist temporal classification (CTC) based streaming ASR, which obtains a good balance between reduced latency and high recognition accuracy. In this paper, we present CUSIDE-T, which successfully adapts the CUSIDE method over the recurrent neural network transducer (RNN-T) ASR architecture, instead of being based on the CTC architecture. We also incorporate language model rescoring in CUSIDE-T to further enhance accuracy, while only bringing a small additional latency. Extensive experiments are conducted over the AISHELL-1, WenetSpeech and SpeechIO datasets, comparing CUSIDE-T and U2++ (both based on RNN-T). U2++ is an existing counterpart of chunk based streaming ASR method. It is shown that CUSIDE-T achieves superior accuracy performance for streaming ASR, with equal settings of latency.