  Neural transducers provide a natural way of streaming ASR. However, they
augment output sequences with blank tokens which leads to challenges for domain
adaptation using text data. This paper proposes a label-synchronous neural
transducer (LS-Transducer), which extracts a label-level encoder representation
before combining it with the prediction network output. Hence blank tokens are
no longer needed and the prediction network can be easily adapted using text
data. An Auto-regressive Integrate-and-Fire (AIF) mechanism is proposed to
generate the label-level encoder representation while retaining the streaming
property. In addition, a streaming joint decoding method is designed to improve
ASR accuracy. Experiments show that compared to standard neural transducers,
the proposed LS-Transducer gave a 10% relative WER reduction (WERR) for
intra-domain Librispeech-100h data, as well as 17% and 19% relative WERRs on
cross-domain TED-LIUM2 and AESRC2020 data with an adapted prediction network.
