# Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition

arXiv: https://arxiv.org/abs/2311.11353 · HTML: https://mlsanity.com/paper/2311.11353/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [Transducer-Llama: Integrating LLMs into Streamable Transducer-based Speech Recognition](https://mlsanity.com/paper/2412.16464/) (2024)
- [SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation](https://mlsanity.com/paper/2504.15509/) (2025)
- [Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges](https://mlsanity.com/paper/2507.18161/) (2025)
- [TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs](https://mlsanity.com/paper/2604.08384/) (2026)
- [Progressive Alignment Objectives for Aligner-Encoder based ASR](https://mlsanity.com/paper/2606.24147/) (2026)

## Abstract (arXiv)

Although end-to-end (E2E) automatic speech recognition (ASR) has shown state-of-the-art recognition accuracy, it tends to be implicitly biased towards the training data distribution which can degrade generalisation. This paper proposes a label-synchronous neural transducer (LS-Transducer), which provides a natural approach to domain adaptation based on text-only data. The LS-Transducer extracts a label-level encoder representation before combining it with the prediction network output. Since blank tokens are no longer needed, the prediction network performs as a standard language model, which can be easily adapted using text-only data. An Auto-regressive Integrate-and-Fire (AIF) mechanism is proposed to generate the label-level encoder representation while retaining low latency operation that can be used for streaming. In addition, a streaming joint decoding method is designed to improve ASR accuracy while retaining synchronisation with AIF. Experiments show that compared to standard neural transducers, the proposed LS-Transducer gave a 12.9% relative WER reduction (WERR) for intra-domain LibriSpeech data, as well as 21.4% and 24.6% relative WERRs on cross-domain TED-LIUM 2 and AESRC2020 data with an adapted prediction network.